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Safety monitoring data classification method based on wireless rough network of neighborhood rough sets
Safety Science 118 (2019) 103–108
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Safety Science journal homepage: www.elsevier.com/locate/safety
Safety monitoring data classification method based on wireless rough network of neighborhood rough sets
T
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Dan Liu , Jingwei Li Department of Computer Science & Technology, Henan Institute of Technology, Xinxiang, Henan 453003, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Data classification Wireless sensor network Neighborhood rough set Local optimal solution
The problem of accurate classification of wireless sensor network data is studied. The data attributes of wireless sensor network are highly redundant. The traditional BP neural network is easy to fall into local optimal solution, poor generalization ability and slow convergence. With the low precision and other problems, it is difficult to accurately classify the data, and a classification algorithm using neighborhood rough sets is proposed. The neighborhood rough set model applies the rough set theory to the neighborhood system. Based on the sample points and their neighborhood radius, the distribution of the entire unbalanced data set in the feature space can be easily obtained. The simulation results show that the algorithm accelerates the convergence speed of the network, and the accuracy of sensor network data classification and recognition has also been greatly improved.
1. Introduction Data classification is an important direction in the field of data mining research. The purpose of classification is to find the commonality of similar data objects from the database sample data, and then construct a classification model (otherwise called a classifier) to describe and characterize the data objects of unknown categories to some of the known categories (Oudre et al., 2012; Su and Wen, 2005; Li et al., 2012a; Yang et al., 2009). One. Among many data classification algorithms, neural network classification methods that have been widely used in the classification field have advantages that other classification methods such as memory association, highly distributed parallel processing, and nonlinear mapping do not have. Data classification is an important direction in the field of data mining research (Li et al., 2012b; Xie et al., 2011; Jiang and Lin, 2011; Banks et al., 2014; Reddy et al., 2015; Shaw et al., 2011). The purpose of classification is to find the commonality of similar data objects from the database sample data, and then construct a classification model (otherwise called a classifier) to describe and characterize the data objects of unknown categories to some of the known categories (Deng et al., 2011; Li et al., 2015; Lak et al., 2016; Rasmussen and Morrissey, 2010). One among many data classification algorithms, neural network classification methods that have been widely used in the classification field have advantages that other classification methods such as memory association, highly distributed parallel processing, and nonlinear mapping do not have
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(Wagner et al., 2014; Denschlag et al., 2013; Zhang et al., 2015; Rivière et al., 2015; Bernardi et al., 2015). However, neural networks are prone to fall into local optimal solutions, poor generalization ability, slow convergence speed and low precision, which limits their application in the classification field (Wu and Chen, 2016). Rough set theory is a mathematical tool for dealing with fuzzy and uncertain knowledge (Achary et al., 2019). Its main idea is to derive decision-making or classification rules by reducing the knowledge under the premise of keeping the knowledge classification ability unchanged (Arunkumar et al., 2017). It is widely used in data mining. machine learning, decision analysis, artificial intelligence, pattern recognition and other fields. Because gene expression values are continuous real data and Pawlak rough sets cannot directly process continuous data, discretization algorithms are needed to convert continuous attributes into discrete attributes (Li et al., 2012b). This process will inevitably lead to information loss, and the result of the calculation process depends to a large extent on the effect of discretization. The neighborhood rough set is a method developed on the basis of the classical rough set theory model, which can directly process continuous data. It does not need to discretize continuous data in advance and can be directly applied to cancer characteristic gene selection. There is no information loss problem before gene reduction, which makes the selected feature gene subset have stronger classification ability. The neighborhood rough set model forms a δ neighborhood with each point in the real space, while the δ neighborhood forms the
Corresponding author. E-mail address: [email protected] (D. Liu).
https://doi.org/10.1016/j.ssci.2019.05.004 Received 1 March 2019; Received in revised form 25 April 2019; Accepted 6 May 2019 Available online 14 May 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.
Safety Science 118 (2019) 103–108
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the in-network information processing capability to aggregate compressed data, achieve data synchronization and task collaborative processing, reduce network data transmission, and thus extend network life (Denschlag et al., 2013). 3. Resource planning. The resource planning of wireless sensor networks for dynamic environment monitoring includes two levels: task-oriented resource planning and resource planning for deployment. Task-oriented resource planning determines whether the node enters sleep or becomes a backbone node according to the constraints of the number of neighbor nodes, the remaining energy of the node, and the availability of the node, without affecting the topology and connectivity of the existing network (Zhang et al., 2015; Rivière et al., 2015; Bernardi et al., 2015).
basic information particles describing any concept in the space (Xie et al., 2011; Jiang and Lin, 2011; Banks et al., 2014; Reddy et al., 2015). For any subset of the space, it can be approximated by the basic information particles. 2. Wireless sensor network monitoring In environmental monitoring applications, wireless sensor networks generally report data in a data-centric manner. The communication cost of sensor nodes in energy consumption is far greater than the cost of calculation, and a large number of sensor nodes are often densely deployed in the monitoring area. The information collected by neighboring nodes for the same event is usually highly correlated and similar (Shaw et al., 2011; Deng et al., 2011; Li et al., 2015). If each sensor node reports event information directly to the base station, the efficiency is very low. At the same time, when the network scale reaches a certain level, a large amount of redundant information causes severe congestion to the limited bandwidth, and even causes network congestion, which also causes great pressure on the data processing of the base station. Therefore, in the process of sensing data processing and transmission, it is necessary to consider how to make full use of the temporal and spatial correlation between the perceptual data, and to minimize the transmission amount of redundant data and the cost of data processing overhead. For wireless sensor nodes, the relationship between the amount of sampled data and the energy consumption is usually weighed. When the sensor node is located near the boundary of the monitoring target area, it only needs to send the perceived event information to the next node in the multi-hop route, and the node in the vicinity of the base station in the monitoring target area is not only to send the event information collected by itself. It also forwards the information sent to it by other nodes, and the energy consumption is much larger than the nodes near the boundary of the monitoring target area. Therefore, the information collected by the sensor node is necessary to be properly aggregated and compressed, and then transmitted back to the base station through multi-hop routing. Intel Labs performed compression experiments on the original acquired data by Lempel-Ziv and Huffman algorithms. The results show that the compressed data traffic of the original collected data is reduced by 2–4 orders of magnitude before the data compression. As a new research field, wireless sensor networks for dynamic environment monitoring have presented a large number of challenging research topics in both technology and basic theory. The solution to these problems is to accelerate the practical application of wireless sensor networks for dynamic environment monitoring. Specific issues are manifested in the following areas:
Seek a reasonable task node allocation scheme to ensure the timely completion of tasks and the energy optimization goals of the network. The goal of resource-oriented deployment is to optimize the spatial resources of the wireless sensor network, so that each point in the monitoring target area can be covered by no less than one node, thus ensuring the completion of information collection, information processing and information transmission tasks. To achieve maximum network coverage and network life. In the wireless sensor network, since the node has the dual functions of information perception and information transmission, the resource planning of the wireless sensor network should achieve the following two objectives: (1) full coverage of the target monitoring area, and the number and consumption of sensor nodes participating in the work. The energy is kept as small as possible; (2) the sensor nodes can communicate with each other to complete the smooth transmission of the sensing information and control commands in the network, which is related to the communication range (radio range) coverage of the sensor node and the energy consumption of the sensor node communication problem (Wu and Chen, 2016). In addition, for the trade-off between communication connection and communication coverage, it is also necessary to use a certain optimization method for coordination. Event awareness and collection are the basic functions of wireless sensor network dynamic environment monitoring applications. The collection method of events can be divided into three types according to the application: query mode, timing report mode and event trigger mode. The query mode is that the sensor node does not actively report the perceived event, but only sends the information when the user issues the query command; the timing report mode is based on the set time interval, and the sensor node periodically reports the perceived event information to the sink node; The trigger mode is that the sensor node compares the perceived information with a preset threshold, and sends the information to the aggregation node only when the collected information exceeds the threshold. In practical applications, these three modes are often used in combination. For example, in the urban intelligent traffic monitoring system, the sensor nodes in various traffic facilities are arranged to report the perceived traffic related information to the collecting node, and the information constitutes the running state of the entire traffic system; when the user wants to pay attention to a certain road segment In the case of traffic conditions, a query request is sent to obtain the latest information on the traffic conditions of the road section; when the traffic volume of a certain road section exceeds the threshold, the event information is quickly reported to the monitoring center. At the same time, in actual applications, overall information may be needed sometimes, and sometimes only local information may be needed. To implement the need to set the perceptual event information of the interest area to a highly flexible and dynamic data information distribution and collection processing mechanism. At present, many data acquisition systems and algorithms for solving wireless sensor networks have been proposed at home and abroad, and a large amount of research experimental data is provided. However, for different application backgrounds of algorithms and systems, they are different in
1. Data transmission. Dynamic environment monitoring places high demands on the real-time and synchronization of transmission. The wireless sensor network for dynamic environment monitoring should have stronger data transmission capability. At present, the processing power and bandwidth resources of wireless sensor networks for dynamic environment monitoring are still very limited. Whether it can effectively solve the problem of timely transmission of monitoring data is also the key to the practical application of wireless sensor networks for dynamic environmental monitoring. 2. Data processing. The data perceived by the wireless sensor network usually has a strong time and spatial correlation, which leads to the transmission of a large amount of redundant information in the network, which inevitably causes a large waste of network resources (Lak et al., 2016; Rasmussen and Morrissey, 2010; Wagner et al., 2014). It is necessary to study how to implement the storage and access mechanism of perceptual data. The core is to determine the location of the data generated by the node in the network, including how to store the data in the appropriate location and how to query the request to the storage location and obtain relevant data. And use 104
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terms of space-time complexity, network topology, energy consumption, and so on.
attribute ai , the Minkowsky distance of the two samples can be defined as:
3. Data classification method using neighborhood rough sets
⎞ ⎛ Δp (x1, x2) = ⎜∑ |f (x1, ai ) − f (x2 , ai )|p ⎟ ⎠ ⎝ i=1
1/ p
m
3.1. Neighborhood model
Among them, i = 1, 2, ...,m , when P = 2 , is the Euler distance.
Rough set theory was proposed by the Polish mathematician Pawlak in the 1970s and is a mathematical theory used to express incompleteness and uncertainty. It not only analyzes and processes a variety of information such as inaccuracy, incompleteness, inconsistency, but also discovers implicit knowledge. As a new subject with a short span of more than 20 years, the research on rough set theory has successfully created a relatively complete theoretical system, and has achieved many excellent systems in system application, rough set model calculation and rough set theory. Results. In rough set theory, knowledge is considered to be an ability to classify abstract objects or realities. In 1988, Lin proposed a neighborhood model that granulates the universe by the neighborhood of the spatial points. A neighborhood is understood as a basic information particle that is used to describe other concepts in space. The neighborhood rough set model is an extended model of the classical rough set theory by Hu et al. using the neighborhood model. Each point in the real space of the model forms an δ neighborhood, and the δ neighborhood family constitutes the description space. The basic information particle of any concept. Combined with the neighborhood rough set model, we present the relevant definition of the neighborhood hypergraph here. The flow is shown in Fig. 1.
The Euler distance is only suitable for calculating continuous attributes and cannot calculate the type attribute. For the calculation of subtyped attributes, Stanfill and Waltz proposed Value Difference Metric (VDM). Suppose the samples x1, x2 are in the two values V1, V2 of the subtype attribute, and the distance between them is defined as: n
f (x1, v1) − f (x1, v2) =
∑ i=1
C1i C − 2i C1 C2
k
(7)
C1 is the number of V1 in the attribute value of all samples, C1i is the number of i , C2 is the number of V2 in all samples, and C2i is i in the category. k is a constant, usually 1. Definition 2. Let G = < X , E > be a neighborhood hypergraph, where X = {x1, x2 , ...,x n} is the set of vertices of the neighborhood hypergraph G , indicating that the neighborhood hypergraph has n vertices; E = {e1, e2, ...,en} is the super-edge set, each element in E Is a superedge connecting ei vertices x i1, x i2 , ...,x ik ; C = {c1, c2, ...,cm} is the superedge attribute set, and D is the super-edge decision classification. The vertex x i is a sample. Definition 3. Let x = {c1 (x ), c2 (x ), ...,cn (x ), D (x ), δ } be a sample, where c1 (x ), c2 (x ), ...,cn (x ) denotes the attribute value of sample x on attribute set C , D (x ) denotes the decision classification of x , and δ is the neighborhood radius of x .
Definition 1. The neighborhood δB (x i ) of any given x i ∈ U , B ⊆ C , x i on the attribute subset B is defined as:
δB (x i ) = {x j |x j ∈ U , ΔB (x i , x j ) ⩽ δ }
(6)
δC (x ) = {e |(e ∈ E ) ∧ Δ(e , x ) ⩽ δ }
(1)
(8)
Definition 4. Given the neighborhood hypergraph G = < X , E >, the super-edge set contained in sample x on attribute set C is:
Here Δ is a metric function that satisfies
Δ(x1, x2) ⩾ 0
(2)
δC (x ) = {e |(e ∈ E ) ∧ Δ(e , x ) ⩽ δ }
Δ(x1, x2) = 0, and x1 = x2
(3)
Δ(x1, x2) = Δ(x2 , x1)
(4)
Δ(x1, x3) ⩽ Δ(x1, x2) + Δ(x2 , x3)
(5)
Definition 5. Given Hypergraph G = < X , E > , ∀ e ∈ E , on attribute set B (B ⊆ C ) , the sample set of associated super-edge e can be expressed as: inB (e ) = {x |e ∈ δ (x ), x ∈ X } , ∀ Y ⊆ E The set of samples associated with super-edge set Y on attribute set B (B ⊆ C ) is inB (Y ) = {inB (e )|e∈Y } .
Considering that x1, x2 is two m dimensional space samples A = {a1, a2 , ...,am} and f (x , ai ) is the value of the sample x on the
(9)
Definition 6. Given a super graph G = < X , E > , ∀ e ∈ E , there is D (e ) ∈ {P , N } , where P represents a small class decision and N represents a large class decision. Then the set of decisions P and N in the super-edge set E are:
nE (P ) = {e |D (e ) = P , e ∈ E }, nE (N ) = {e |D (e ) = N , e ∈ E }
(10)
And the imbalance of the super-edge set is:
inbanE =
|nE (N )| |nE (P )|
(11)
3.2. Training sample classification Classification is to classify the sample set with the current superedge set (training set the latter test set). Through the analysis of the classification result, check whether the accuracy of the super-edge set classification meets the specified requirements, and determine whether the current super-edge set needs Continue to learn about the training set. By classifying the samples and learning the loop of the training set, the super-edge set is constantly approaching the distribution pattern of the sample set. In the sample classification and super-edge learning in this paper, the process of judging the sample categories by super-edge is
Fig. 1. Neighborhood rough set model flow. 105
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mainly consumed in the data back-transmission, and the energy consumed by the relative node is expensive. In many cases, in the dynamic environment monitoring, a large number of sensor nodes are deployed in the target monitoring area. The single node sensing data is not indispensable for the collecting nodes, and the end users generally do not pay attention to which specific sensor. What monitoring data is collected by the node, and the neighboring nodes are often very similar or even identical to the same event. For example, in the actual application of forest fire monitoring, the end user is only interested in the maximum temperature in the monitoring area. In the process of data collection and backhaul, the sensor node only needs to receive and send the maximum value, so data aggregation can be performed. Aggregation, that is, the in-network aggregation processing capability is used to aggregate the data perceived by the node itself and received from other sensor nodes to eliminate information redundancy, reduce data transmission, save energy consumption, and prolong network working life. There is a close relationship between data aggregation and network routing protocols. The wireless sensor network routing protocol is mainly concerned with how to transmit the monitoring data perceived by the sensor nodes to the base station under the premise of certain quality assurance. In the design of network routing protocols, from the perspective of energy consumption, real-time and reliability, it is necessary to find a feasible path for each sensor node to return data, and minimize the energy consumption of the wireless sensor network. In dynamic environment monitoring, the deployment of sensor nodes is generally very dense, and the monitoring information perceived by neighboring nodes has a great correlation, resulting in a large amount of redundant data. Data aggregation technology mainly focuses on how to reduce or even eliminate large amounts of data in the network. Redundancy, improve network energy efficiency and communication efficiency, and try to extend network life. Therefore, it is necessary to combine data aggregation and network routing protocols, and fully consider data aggregation requirements in routing protocol design. In designing network routing, it is necessary to pay attention to the backhaul requirement of a single data packet, and also consider the data aggregation requirements of as many data packets as possible, and also fully consider the network routing protocol when designing the data aggregation structure. The combination of data aggregation and network routing protocols can reduce the communication cost of data packet backhaul in wireless sensor networks, which can greatly reduce network energy consumption and thus extend network lifetime.
the same, so only the specific classification algorithm is given here. The algorithm determines the samples into large categories by the cost of the sample classification. Still a small class. The training sample classification is to classify the training set with the generated super-edge set. Through the analysis of the classification result, it is checked whether the accuracy of the super-edge set classification meets the specified requirements, and whether the super-edge set needs to be replaced by the super-edge is passed. The loop of training sample classification and super-edge substitution is used to make the generated super-edge set continuously approach the distribution pattern of the training set. Definition 7 Given the super-edges in the neighborhood of the given hypergraph G = < X , E > , ∀ x ∈ X , the large-class super-edge set and the small-class super-edge set in the sample x neighborhood can be expressed as:
ne (x , N ) = {e |D (e ) = N , e ∈ δ (x )}, ne (x , P ) = {e |D (e ) = P , e ∈ δ (x )} (12) In the process of classifying super-maps in the neighborhood, two factors need to be considered: (1) the number of super-large classes and small classes in the neighborhood of the sample; and (2) the imbalance of the super-edge set of the neighborhood hypergraph. Combining these two factors, we give a classification method for the sample. Given sample set ∀ x ∈ X , attribute set B ⊆ C :
If
ne (x , N ) ⩾ inbanE 2, ne (x , P )
then D (x ) = N ;
If
ne (x , N ) < inbanE 2, ne (x , P )
then D (x ) = P;
The training set is classified according to the above classification principle. If the correct rate is greater than the given threshold of 0.95, the super-edge set is output; otherwise, the super-edge substitution process will be required. Feature selection is often referred to as attribute reduction in rough set theory. Generally, the heuristic search feature selection algorithm based on rough set model and its generalization model mainly includes positive domain-based feature selection, distinguishing matrix-based feature selection, combined mode-based feature selection and entropy based. Feature selection. This paper first clarifies the dependency metrics on the subset of attributes, and then selects features based on attribute dependencies on the feature subspace, and outputs feature subsets. In the application of dynamic environment monitoring in wireless sensor networks, unlike the characteristics of traditional networks, it brings many new problems and challenges to its intranet information processing. For example, there are a large number of nodes in the network, and the distribution is dense. The information processing algorithms in the network are required to be highly scalable. The sensor nodes have limited energy and are powered by batteries. The nodes are generally unattended, energy is limited and cannot be supplemented, so energy is saved and the network is extended. The life cycle is a very important issue, requiring the information processing algorithm in the network to have good adaptability and robustness; data-centric, it is required to process the data content delivered while routing; close to the specific application Correlation, the information processing method must correspond to a specific application, and there is no universal unified processing method. These require the construction of a data-centric, intra-network information processing model that maximizes network lifecycle and collaboration. Therefore, the information processing method in the traditional wireless network cannot be directly used in the wireless sensor network, and it is necessary to study the intra-network information processing technology suitable for the characteristics of the wireless sensor network. The wireless sensor network generally adopts a data-centric routing mechanism to transmit the collected data back to the base station through self-organizing multi-hop, and the energy of the sensor node is
4. Experiment analysis 4.1. Experiment setup In the Ubuntu-12.04.0 system, the data of Breast Cancer, Wisconsin diagnostic breast cancer (WDBC) and Wisconsin prognostic breast cancer (WPBC) in the UCI data set were used as experimental subjects. The conditional attributes of the three sets of data are all numeric and are two-category data. Before the feature selection, in order to avoid the influence of the attribute value dimension inconsistency, all attributes are normalized and mapped to the [0, 1] interval. 4.2. Analysis of classification accuracy In order to explore the influence of feature selection algorithm on the subsequent classification process, this paper uses the neighborhood rough set as the classification algorithm. The NR-FRS Reduction algorithm, the NFARNRS algorithm, and the KPCA-based feature selection (or dimensionality reduction) algorithm are compared. Using the feature subset obtained by the feature selection algorithm as input, a 10layer cross-validation classification was performed on the Breast cancer, WDBC, and WPBC data sets, and the classification accuracy was compared. The NFARNRS (Naive Forward Attribute Reduction Based on 106
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Neighborhood Rough Set model) algorithm in the literature (Wagner et al., 2014; Denschlag et al., 2013) and the NR-FRS Reduction algorithm in this paper are all feature selection algorithms related to parameters. They correspond to two parameters, the neighborhood radius r and the positive domain gain threshold α , respectively. The choice of parameters is critical to the execution of the algorithm. At present, relevant researchers have explored the method of setting the neighborhood radius r from some angles, but in most cases, the value is determined by experimental analysis, and the value of r is related to the specific classification problem. Like the neighborhood radius r, this paper first explores the relationship between the value of parameter α and the result of attribute selection in the NR-FRS Reduction algorithm. The following is an analysis and comparison of the attribute selection results produced by the parameters r and α in their fixed range of values. Using Breast cancer, WDBC, WPBC data sets as the reduction object, first normalize the data between [0, 1], then set the interval of r to [0.05, 1], and the interval of α is [0, 10]. Figs. 1–3 depict the number of reduction set attributes for the three sets of data sets as a function of parameters r and α . It can be seen from Figs. 2–4 that for the NFARNRS algorithm using the parameter r, when r is initially small, the number of selected features is also small. Within a certain range, as the radius of the neighborhood increases, the number of features required gradually increases. However, when r exceeds a certain value, the number of features drops sharply. This phenomenon is in line with the actual law, r can be seen as a granulation scale to describe the problem, when it is small, only a small number of attributes can be used to classify the problem. As this scale increases, describing the problem requires more attributes. When the granulation scale exceeds a certain value, no matter how many attributes, the classification problem cannot be characterized, and the number of attributes is kept at a low level. The classification accuracy of the feature subsets obtained by the proposed method is more stable than that of the NFARNRS algorithm and the KPCA-based feature selection algorithm. At the same time, it is not difficult to find that the corresponding classification accuracy rate is higher in the classification accuracy corresponding to some parameter points than the latter two.
Fig. 2. Classification accuracy varies with positive domain gain threshold.
5. Conclusion As an important part of data mining, the role of data preprocessing is crucial. In pattern recognition, machine learning, and data mining, feature selection is an important technology that has attracted widespread attention in recent years. Due to the development of information acquisition and storage technologies, databases in practical applications sometimes store data with dozens, hundreds or even hundreds of thousands of features. Faced with a limited training data set, a large number of features can seriously slow down the entire learning process, and the classifier may also face over-fitting of the training data set, which is closely related to the impact of the relevant redundant features on the classifier. During the big data period, information may be fed back from multiple perspectives. For learning tasks, different sources provide complementary information.
Fig. 3. Classification accuracy varies with neighborhood radius.
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Fig. 4. Classification accuracy rate varies with Gaussian kernel parameters.
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Safety monitoring data classification method based on wireless rough network of neighborhood rough sets
T
⁎
Dan Liu , Jingwei Li Department of Computer Science & Technology, Henan Institute of Technology, Xinxiang, Henan 453003, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Data classification Wireless sensor network Neighborhood rough set Local optimal solution
The problem of accurate classification of wireless sensor network data is studied. The data attributes of wireless sensor network are highly redundant. The traditional BP neural network is easy to fall into local optimal solution, poor generalization ability and slow convergence. With the low precision and other problems, it is difficult to accurately classify the data, and a classification algorithm using neighborhood rough sets is proposed. The neighborhood rough set model applies the rough set theory to the neighborhood system. Based on the sample points and their neighborhood radius, the distribution of the entire unbalanced data set in the feature space can be easily obtained. The simulation results show that the algorithm accelerates the convergence speed of the network, and the accuracy of sensor network data classification and recognition has also been greatly improved.
1. Introduction Data classification is an important direction in the field of data mining research. The purpose of classification is to find the commonality of similar data objects from the database sample data, and then construct a classification model (otherwise called a classifier) to describe and characterize the data objects of unknown categories to some of the known categories (Oudre et al., 2012; Su and Wen, 2005; Li et al., 2012a; Yang et al., 2009). One. Among many data classification algorithms, neural network classification methods that have been widely used in the classification field have advantages that other classification methods such as memory association, highly distributed parallel processing, and nonlinear mapping do not have. Data classification is an important direction in the field of data mining research (Li et al., 2012b; Xie et al., 2011; Jiang and Lin, 2011; Banks et al., 2014; Reddy et al., 2015; Shaw et al., 2011). The purpose of classification is to find the commonality of similar data objects from the database sample data, and then construct a classification model (otherwise called a classifier) to describe and characterize the data objects of unknown categories to some of the known categories (Deng et al., 2011; Li et al., 2015; Lak et al., 2016; Rasmussen and Morrissey, 2010). One among many data classification algorithms, neural network classification methods that have been widely used in the classification field have advantages that other classification methods such as memory association, highly distributed parallel processing, and nonlinear mapping do not have
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(Wagner et al., 2014; Denschlag et al., 2013; Zhang et al., 2015; Rivière et al., 2015; Bernardi et al., 2015). However, neural networks are prone to fall into local optimal solutions, poor generalization ability, slow convergence speed and low precision, which limits their application in the classification field (Wu and Chen, 2016). Rough set theory is a mathematical tool for dealing with fuzzy and uncertain knowledge (Achary et al., 2019). Its main idea is to derive decision-making or classification rules by reducing the knowledge under the premise of keeping the knowledge classification ability unchanged (Arunkumar et al., 2017). It is widely used in data mining. machine learning, decision analysis, artificial intelligence, pattern recognition and other fields. Because gene expression values are continuous real data and Pawlak rough sets cannot directly process continuous data, discretization algorithms are needed to convert continuous attributes into discrete attributes (Li et al., 2012b). This process will inevitably lead to information loss, and the result of the calculation process depends to a large extent on the effect of discretization. The neighborhood rough set is a method developed on the basis of the classical rough set theory model, which can directly process continuous data. It does not need to discretize continuous data in advance and can be directly applied to cancer characteristic gene selection. There is no information loss problem before gene reduction, which makes the selected feature gene subset have stronger classification ability. The neighborhood rough set model forms a δ neighborhood with each point in the real space, while the δ neighborhood forms the
Corresponding author. E-mail address: [email protected] (D. Liu).
https://doi.org/10.1016/j.ssci.2019.05.004 Received 1 March 2019; Received in revised form 25 April 2019; Accepted 6 May 2019 Available online 14 May 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.
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the in-network information processing capability to aggregate compressed data, achieve data synchronization and task collaborative processing, reduce network data transmission, and thus extend network life (Denschlag et al., 2013). 3. Resource planning. The resource planning of wireless sensor networks for dynamic environment monitoring includes two levels: task-oriented resource planning and resource planning for deployment. Task-oriented resource planning determines whether the node enters sleep or becomes a backbone node according to the constraints of the number of neighbor nodes, the remaining energy of the node, and the availability of the node, without affecting the topology and connectivity of the existing network (Zhang et al., 2015; Rivière et al., 2015; Bernardi et al., 2015).
basic information particles describing any concept in the space (Xie et al., 2011; Jiang and Lin, 2011; Banks et al., 2014; Reddy et al., 2015). For any subset of the space, it can be approximated by the basic information particles. 2. Wireless sensor network monitoring In environmental monitoring applications, wireless sensor networks generally report data in a data-centric manner. The communication cost of sensor nodes in energy consumption is far greater than the cost of calculation, and a large number of sensor nodes are often densely deployed in the monitoring area. The information collected by neighboring nodes for the same event is usually highly correlated and similar (Shaw et al., 2011; Deng et al., 2011; Li et al., 2015). If each sensor node reports event information directly to the base station, the efficiency is very low. At the same time, when the network scale reaches a certain level, a large amount of redundant information causes severe congestion to the limited bandwidth, and even causes network congestion, which also causes great pressure on the data processing of the base station. Therefore, in the process of sensing data processing and transmission, it is necessary to consider how to make full use of the temporal and spatial correlation between the perceptual data, and to minimize the transmission amount of redundant data and the cost of data processing overhead. For wireless sensor nodes, the relationship between the amount of sampled data and the energy consumption is usually weighed. When the sensor node is located near the boundary of the monitoring target area, it only needs to send the perceived event information to the next node in the multi-hop route, and the node in the vicinity of the base station in the monitoring target area is not only to send the event information collected by itself. It also forwards the information sent to it by other nodes, and the energy consumption is much larger than the nodes near the boundary of the monitoring target area. Therefore, the information collected by the sensor node is necessary to be properly aggregated and compressed, and then transmitted back to the base station through multi-hop routing. Intel Labs performed compression experiments on the original acquired data by Lempel-Ziv and Huffman algorithms. The results show that the compressed data traffic of the original collected data is reduced by 2–4 orders of magnitude before the data compression. As a new research field, wireless sensor networks for dynamic environment monitoring have presented a large number of challenging research topics in both technology and basic theory. The solution to these problems is to accelerate the practical application of wireless sensor networks for dynamic environment monitoring. Specific issues are manifested in the following areas:
Seek a reasonable task node allocation scheme to ensure the timely completion of tasks and the energy optimization goals of the network. The goal of resource-oriented deployment is to optimize the spatial resources of the wireless sensor network, so that each point in the monitoring target area can be covered by no less than one node, thus ensuring the completion of information collection, information processing and information transmission tasks. To achieve maximum network coverage and network life. In the wireless sensor network, since the node has the dual functions of information perception and information transmission, the resource planning of the wireless sensor network should achieve the following two objectives: (1) full coverage of the target monitoring area, and the number and consumption of sensor nodes participating in the work. The energy is kept as small as possible; (2) the sensor nodes can communicate with each other to complete the smooth transmission of the sensing information and control commands in the network, which is related to the communication range (radio range) coverage of the sensor node and the energy consumption of the sensor node communication problem (Wu and Chen, 2016). In addition, for the trade-off between communication connection and communication coverage, it is also necessary to use a certain optimization method for coordination. Event awareness and collection are the basic functions of wireless sensor network dynamic environment monitoring applications. The collection method of events can be divided into three types according to the application: query mode, timing report mode and event trigger mode. The query mode is that the sensor node does not actively report the perceived event, but only sends the information when the user issues the query command; the timing report mode is based on the set time interval, and the sensor node periodically reports the perceived event information to the sink node; The trigger mode is that the sensor node compares the perceived information with a preset threshold, and sends the information to the aggregation node only when the collected information exceeds the threshold. In practical applications, these three modes are often used in combination. For example, in the urban intelligent traffic monitoring system, the sensor nodes in various traffic facilities are arranged to report the perceived traffic related information to the collecting node, and the information constitutes the running state of the entire traffic system; when the user wants to pay attention to a certain road segment In the case of traffic conditions, a query request is sent to obtain the latest information on the traffic conditions of the road section; when the traffic volume of a certain road section exceeds the threshold, the event information is quickly reported to the monitoring center. At the same time, in actual applications, overall information may be needed sometimes, and sometimes only local information may be needed. To implement the need to set the perceptual event information of the interest area to a highly flexible and dynamic data information distribution and collection processing mechanism. At present, many data acquisition systems and algorithms for solving wireless sensor networks have been proposed at home and abroad, and a large amount of research experimental data is provided. However, for different application backgrounds of algorithms and systems, they are different in
1. Data transmission. Dynamic environment monitoring places high demands on the real-time and synchronization of transmission. The wireless sensor network for dynamic environment monitoring should have stronger data transmission capability. At present, the processing power and bandwidth resources of wireless sensor networks for dynamic environment monitoring are still very limited. Whether it can effectively solve the problem of timely transmission of monitoring data is also the key to the practical application of wireless sensor networks for dynamic environmental monitoring. 2. Data processing. The data perceived by the wireless sensor network usually has a strong time and spatial correlation, which leads to the transmission of a large amount of redundant information in the network, which inevitably causes a large waste of network resources (Lak et al., 2016; Rasmussen and Morrissey, 2010; Wagner et al., 2014). It is necessary to study how to implement the storage and access mechanism of perceptual data. The core is to determine the location of the data generated by the node in the network, including how to store the data in the appropriate location and how to query the request to the storage location and obtain relevant data. And use 104
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terms of space-time complexity, network topology, energy consumption, and so on.
attribute ai , the Minkowsky distance of the two samples can be defined as:
3. Data classification method using neighborhood rough sets
⎞ ⎛ Δp (x1, x2) = ⎜∑ |f (x1, ai ) − f (x2 , ai )|p ⎟ ⎠ ⎝ i=1
1/ p
m
3.1. Neighborhood model
Among them, i = 1, 2, ...,m , when P = 2 , is the Euler distance.
Rough set theory was proposed by the Polish mathematician Pawlak in the 1970s and is a mathematical theory used to express incompleteness and uncertainty. It not only analyzes and processes a variety of information such as inaccuracy, incompleteness, inconsistency, but also discovers implicit knowledge. As a new subject with a short span of more than 20 years, the research on rough set theory has successfully created a relatively complete theoretical system, and has achieved many excellent systems in system application, rough set model calculation and rough set theory. Results. In rough set theory, knowledge is considered to be an ability to classify abstract objects or realities. In 1988, Lin proposed a neighborhood model that granulates the universe by the neighborhood of the spatial points. A neighborhood is understood as a basic information particle that is used to describe other concepts in space. The neighborhood rough set model is an extended model of the classical rough set theory by Hu et al. using the neighborhood model. Each point in the real space of the model forms an δ neighborhood, and the δ neighborhood family constitutes the description space. The basic information particle of any concept. Combined with the neighborhood rough set model, we present the relevant definition of the neighborhood hypergraph here. The flow is shown in Fig. 1.
The Euler distance is only suitable for calculating continuous attributes and cannot calculate the type attribute. For the calculation of subtyped attributes, Stanfill and Waltz proposed Value Difference Metric (VDM). Suppose the samples x1, x2 are in the two values V1, V2 of the subtype attribute, and the distance between them is defined as: n
f (x1, v1) − f (x1, v2) =
∑ i=1
C1i C − 2i C1 C2
k
(7)
C1 is the number of V1 in the attribute value of all samples, C1i is the number of i , C2 is the number of V2 in all samples, and C2i is i in the category. k is a constant, usually 1. Definition 2. Let G = < X , E > be a neighborhood hypergraph, where X = {x1, x2 , ...,x n} is the set of vertices of the neighborhood hypergraph G , indicating that the neighborhood hypergraph has n vertices; E = {e1, e2, ...,en} is the super-edge set, each element in E Is a superedge connecting ei vertices x i1, x i2 , ...,x ik ; C = {c1, c2, ...,cm} is the superedge attribute set, and D is the super-edge decision classification. The vertex x i is a sample. Definition 3. Let x = {c1 (x ), c2 (x ), ...,cn (x ), D (x ), δ } be a sample, where c1 (x ), c2 (x ), ...,cn (x ) denotes the attribute value of sample x on attribute set C , D (x ) denotes the decision classification of x , and δ is the neighborhood radius of x .
Definition 1. The neighborhood δB (x i ) of any given x i ∈ U , B ⊆ C , x i on the attribute subset B is defined as:
δB (x i ) = {x j |x j ∈ U , ΔB (x i , x j ) ⩽ δ }
(6)
δC (x ) = {e |(e ∈ E ) ∧ Δ(e , x ) ⩽ δ }
(1)
(8)
Definition 4. Given the neighborhood hypergraph G = < X , E >, the super-edge set contained in sample x on attribute set C is:
Here Δ is a metric function that satisfies
Δ(x1, x2) ⩾ 0
(2)
δC (x ) = {e |(e ∈ E ) ∧ Δ(e , x ) ⩽ δ }
Δ(x1, x2) = 0, and x1 = x2
(3)
Δ(x1, x2) = Δ(x2 , x1)
(4)
Δ(x1, x3) ⩽ Δ(x1, x2) + Δ(x2 , x3)
(5)
Definition 5. Given Hypergraph G = < X , E > , ∀ e ∈ E , on attribute set B (B ⊆ C ) , the sample set of associated super-edge e can be expressed as: inB (e ) = {x |e ∈ δ (x ), x ∈ X } , ∀ Y ⊆ E The set of samples associated with super-edge set Y on attribute set B (B ⊆ C ) is inB (Y ) = {inB (e )|e∈Y } .
Considering that x1, x2 is two m dimensional space samples A = {a1, a2 , ...,am} and f (x , ai ) is the value of the sample x on the
(9)
Definition 6. Given a super graph G = < X , E > , ∀ e ∈ E , there is D (e ) ∈ {P , N } , where P represents a small class decision and N represents a large class decision. Then the set of decisions P and N in the super-edge set E are:
nE (P ) = {e |D (e ) = P , e ∈ E }, nE (N ) = {e |D (e ) = N , e ∈ E }
(10)
And the imbalance of the super-edge set is:
inbanE =
|nE (N )| |nE (P )|
(11)
3.2. Training sample classification Classification is to classify the sample set with the current superedge set (training set the latter test set). Through the analysis of the classification result, check whether the accuracy of the super-edge set classification meets the specified requirements, and determine whether the current super-edge set needs Continue to learn about the training set. By classifying the samples and learning the loop of the training set, the super-edge set is constantly approaching the distribution pattern of the sample set. In the sample classification and super-edge learning in this paper, the process of judging the sample categories by super-edge is
Fig. 1. Neighborhood rough set model flow. 105
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mainly consumed in the data back-transmission, and the energy consumed by the relative node is expensive. In many cases, in the dynamic environment monitoring, a large number of sensor nodes are deployed in the target monitoring area. The single node sensing data is not indispensable for the collecting nodes, and the end users generally do not pay attention to which specific sensor. What monitoring data is collected by the node, and the neighboring nodes are often very similar or even identical to the same event. For example, in the actual application of forest fire monitoring, the end user is only interested in the maximum temperature in the monitoring area. In the process of data collection and backhaul, the sensor node only needs to receive and send the maximum value, so data aggregation can be performed. Aggregation, that is, the in-network aggregation processing capability is used to aggregate the data perceived by the node itself and received from other sensor nodes to eliminate information redundancy, reduce data transmission, save energy consumption, and prolong network working life. There is a close relationship between data aggregation and network routing protocols. The wireless sensor network routing protocol is mainly concerned with how to transmit the monitoring data perceived by the sensor nodes to the base station under the premise of certain quality assurance. In the design of network routing protocols, from the perspective of energy consumption, real-time and reliability, it is necessary to find a feasible path for each sensor node to return data, and minimize the energy consumption of the wireless sensor network. In dynamic environment monitoring, the deployment of sensor nodes is generally very dense, and the monitoring information perceived by neighboring nodes has a great correlation, resulting in a large amount of redundant data. Data aggregation technology mainly focuses on how to reduce or even eliminate large amounts of data in the network. Redundancy, improve network energy efficiency and communication efficiency, and try to extend network life. Therefore, it is necessary to combine data aggregation and network routing protocols, and fully consider data aggregation requirements in routing protocol design. In designing network routing, it is necessary to pay attention to the backhaul requirement of a single data packet, and also consider the data aggregation requirements of as many data packets as possible, and also fully consider the network routing protocol when designing the data aggregation structure. The combination of data aggregation and network routing protocols can reduce the communication cost of data packet backhaul in wireless sensor networks, which can greatly reduce network energy consumption and thus extend network lifetime.
the same, so only the specific classification algorithm is given here. The algorithm determines the samples into large categories by the cost of the sample classification. Still a small class. The training sample classification is to classify the training set with the generated super-edge set. Through the analysis of the classification result, it is checked whether the accuracy of the super-edge set classification meets the specified requirements, and whether the super-edge set needs to be replaced by the super-edge is passed. The loop of training sample classification and super-edge substitution is used to make the generated super-edge set continuously approach the distribution pattern of the training set. Definition 7 Given the super-edges in the neighborhood of the given hypergraph G = < X , E > , ∀ x ∈ X , the large-class super-edge set and the small-class super-edge set in the sample x neighborhood can be expressed as:
ne (x , N ) = {e |D (e ) = N , e ∈ δ (x )}, ne (x , P ) = {e |D (e ) = P , e ∈ δ (x )} (12) In the process of classifying super-maps in the neighborhood, two factors need to be considered: (1) the number of super-large classes and small classes in the neighborhood of the sample; and (2) the imbalance of the super-edge set of the neighborhood hypergraph. Combining these two factors, we give a classification method for the sample. Given sample set ∀ x ∈ X , attribute set B ⊆ C :
If
ne (x , N ) ⩾ inbanE 2, ne (x , P )
then D (x ) = N ;
If
ne (x , N ) < inbanE 2, ne (x , P )
then D (x ) = P;
The training set is classified according to the above classification principle. If the correct rate is greater than the given threshold of 0.95, the super-edge set is output; otherwise, the super-edge substitution process will be required. Feature selection is often referred to as attribute reduction in rough set theory. Generally, the heuristic search feature selection algorithm based on rough set model and its generalization model mainly includes positive domain-based feature selection, distinguishing matrix-based feature selection, combined mode-based feature selection and entropy based. Feature selection. This paper first clarifies the dependency metrics on the subset of attributes, and then selects features based on attribute dependencies on the feature subspace, and outputs feature subsets. In the application of dynamic environment monitoring in wireless sensor networks, unlike the characteristics of traditional networks, it brings many new problems and challenges to its intranet information processing. For example, there are a large number of nodes in the network, and the distribution is dense. The information processing algorithms in the network are required to be highly scalable. The sensor nodes have limited energy and are powered by batteries. The nodes are generally unattended, energy is limited and cannot be supplemented, so energy is saved and the network is extended. The life cycle is a very important issue, requiring the information processing algorithm in the network to have good adaptability and robustness; data-centric, it is required to process the data content delivered while routing; close to the specific application Correlation, the information processing method must correspond to a specific application, and there is no universal unified processing method. These require the construction of a data-centric, intra-network information processing model that maximizes network lifecycle and collaboration. Therefore, the information processing method in the traditional wireless network cannot be directly used in the wireless sensor network, and it is necessary to study the intra-network information processing technology suitable for the characteristics of the wireless sensor network. The wireless sensor network generally adopts a data-centric routing mechanism to transmit the collected data back to the base station through self-organizing multi-hop, and the energy of the sensor node is
4. Experiment analysis 4.1. Experiment setup In the Ubuntu-12.04.0 system, the data of Breast Cancer, Wisconsin diagnostic breast cancer (WDBC) and Wisconsin prognostic breast cancer (WPBC) in the UCI data set were used as experimental subjects. The conditional attributes of the three sets of data are all numeric and are two-category data. Before the feature selection, in order to avoid the influence of the attribute value dimension inconsistency, all attributes are normalized and mapped to the [0, 1] interval. 4.2. Analysis of classification accuracy In order to explore the influence of feature selection algorithm on the subsequent classification process, this paper uses the neighborhood rough set as the classification algorithm. The NR-FRS Reduction algorithm, the NFARNRS algorithm, and the KPCA-based feature selection (or dimensionality reduction) algorithm are compared. Using the feature subset obtained by the feature selection algorithm as input, a 10layer cross-validation classification was performed on the Breast cancer, WDBC, and WPBC data sets, and the classification accuracy was compared. The NFARNRS (Naive Forward Attribute Reduction Based on 106
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Neighborhood Rough Set model) algorithm in the literature (Wagner et al., 2014; Denschlag et al., 2013) and the NR-FRS Reduction algorithm in this paper are all feature selection algorithms related to parameters. They correspond to two parameters, the neighborhood radius r and the positive domain gain threshold α , respectively. The choice of parameters is critical to the execution of the algorithm. At present, relevant researchers have explored the method of setting the neighborhood radius r from some angles, but in most cases, the value is determined by experimental analysis, and the value of r is related to the specific classification problem. Like the neighborhood radius r, this paper first explores the relationship between the value of parameter α and the result of attribute selection in the NR-FRS Reduction algorithm. The following is an analysis and comparison of the attribute selection results produced by the parameters r and α in their fixed range of values. Using Breast cancer, WDBC, WPBC data sets as the reduction object, first normalize the data between [0, 1], then set the interval of r to [0.05, 1], and the interval of α is [0, 10]. Figs. 1–3 depict the number of reduction set attributes for the three sets of data sets as a function of parameters r and α . It can be seen from Figs. 2–4 that for the NFARNRS algorithm using the parameter r, when r is initially small, the number of selected features is also small. Within a certain range, as the radius of the neighborhood increases, the number of features required gradually increases. However, when r exceeds a certain value, the number of features drops sharply. This phenomenon is in line with the actual law, r can be seen as a granulation scale to describe the problem, when it is small, only a small number of attributes can be used to classify the problem. As this scale increases, describing the problem requires more attributes. When the granulation scale exceeds a certain value, no matter how many attributes, the classification problem cannot be characterized, and the number of attributes is kept at a low level. The classification accuracy of the feature subsets obtained by the proposed method is more stable than that of the NFARNRS algorithm and the KPCA-based feature selection algorithm. At the same time, it is not difficult to find that the corresponding classification accuracy rate is higher in the classification accuracy corresponding to some parameter points than the latter two.
Fig. 2. Classification accuracy varies with positive domain gain threshold.
5. Conclusion As an important part of data mining, the role of data preprocessing is crucial. In pattern recognition, machine learning, and data mining, feature selection is an important technology that has attracted widespread attention in recent years. Due to the development of information acquisition and storage technologies, databases in practical applications sometimes store data with dozens, hundreds or even hundreds of thousands of features. Faced with a limited training data set, a large number of features can seriously slow down the entire learning process, and the classifier may also face over-fitting of the training data set, which is closely related to the impact of the relevant redundant features on the classifier. During the big data period, information may be fed back from multiple perspectives. For learning tasks, different sources provide complementary information.
Fig. 3. Classification accuracy varies with neighborhood radius.
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Fig. 4. Classification accuracy rate varies with Gaussian kernel parameters.
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