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Special issue on fuzzy set technology in pattern recognition
Pattern Recognition Letters ELSEVIER
Pattern Recognition Letters 17 (1996) 565-566
Editorial
Special issue on fuzzy set technology in pattern recognition Witold Pedrycz Department of Electrical Engineering, The University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada
Pattern recognition undergoes an important evolution. While retaining its genuine identity, the area exploits and absorbs new technologies of computing including fuzzy computation, neurocomputing and evolutionary computation. The place of fuzzy sets in pattern recognition is definitely visible. In a nutshell, f u z ~ sets are providers of a solid conceptual framework augmenting PR by the level of uncertainty representation. With the increasing complexity of classification problems, the role of fuzzy sets becomes more profound and inevitable as the uncertainty factor tends to become inherently associated with the problems under consideration. Interestingly enough, from their very inception, fuzzy sets were quite much aimed at the very essence of classification and abstraction (Bellman et al., 1966; Bezdek and Pal, 1992). Two interesting principles coined by Marr (1982) can be regarded as a useful leitmotiv emphasizing the general role of fuzzy sets in PR, see also (Keller, 1995). These are the principle of least commitment (Don't do something that may later have to be undone) and the principle of graceful degradation (Degrading the data will not prevent the delivery of at least some of the answer). The quantification of these crucial principles can be conveniently expressed by fuzzy sets being utilized in the formulation of the problem and used afterwards in the ensuing classification activities. This special issue highlights some ongoing research in the realm of Fuzzy Pattern Recognition. The material is kept well-balanced and reports on conceptual as well as applied studies. Elsevier Science B.V, PII S 0 1 6 7 - 8 6 5 5 ( 9 6 ) 0 0 0 1 9 - 0
The idea of fuzzy integrals in pattern classifiers, especially in the realm of feature analysis is studied by Grabisch. This contribution is followed by the paper authored by Gader et al. utilizing the concepts of fuzzy integrals in fusion of classifiers in the domain of handwriting recognition. The way in which fuzzy data are derived from grey-tone images is discussed by Bandemer. The two subsequent papers look into the technology of fuzzy sets as an important vehicle of knowledge acquisition and knowledge representation. Baldwin discusses a concept of fuzzy data browsing while Nakagawa et al. elaborate on fuzzy knowledge bases for understanding dynamic images. Fuzzy clustering, especially C-means, has emerged as a successful technique of unsupervised classification. This area is vigorously pursued in the current research and is also reflected in this issue. Hathaway et al. consider a relational data version of some standard fuzzy clustering algorithms; as opposed to object data, a relational character of patterns implies their description in terms of their pairwise similarities. The validation aspects of C-shells clustering are discussed by Dave. The paper by Pedrycz proposes a certain conditioning of fuzzy clustering and studies its usage in data mining. The issue of noise and outlier sensitivity in the setting of robust clustering is addressed by Kim et al. Sequential fuzzy clustering is applied to image analysis by supporting mechanisms of extracting straight lines as discussed by Tsuda et al. Another interesting application of fuzzy C-means to a wastewater treatment process is studied by Marsili-Libelli and Muller. The
566
W. Pedrycz / Pattern Recognition Letters 17 (1996) 565-566
paper by Bortolan et al. discusses hybrid fuzzy-neuro classifiers in the problems of classification of ECG signals. The authors and reviewers deserve our sincere thanks for their effort of making recent achievements in fuzzy sets available to the PR community. Special thanks go the Editor-in-Chief, Edzard Gelsema, for his continuous encouragement during this project. I hope that the readers will find the papers highly informative, innovative and thought-provoking.
References Bellman, R., R. Kalaba and L. Zadeh (1966). Abstraction and pattern classification. J. Math. Anal. AppL 13, 1-7. Bezdek, J.C. and S.K. Pal, Eds., Fuzzy Models for Pattern Recognition. IEEE Press, New York. Keller, J.M. (1995). Fuzzy sets in computer vision. Proc. VI IFSA World Congress, Sao Panlo, Brazil, Vol. I, 7-10. Marr, D. (1982). Vision. W.H. Freeman, San Francisco, CA.
Pattern Recognition Letters 17 (1996) 565-566
Editorial
Special issue on fuzzy set technology in pattern recognition Witold Pedrycz Department of Electrical Engineering, The University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada
Pattern recognition undergoes an important evolution. While retaining its genuine identity, the area exploits and absorbs new technologies of computing including fuzzy computation, neurocomputing and evolutionary computation. The place of fuzzy sets in pattern recognition is definitely visible. In a nutshell, f u z ~ sets are providers of a solid conceptual framework augmenting PR by the level of uncertainty representation. With the increasing complexity of classification problems, the role of fuzzy sets becomes more profound and inevitable as the uncertainty factor tends to become inherently associated with the problems under consideration. Interestingly enough, from their very inception, fuzzy sets were quite much aimed at the very essence of classification and abstraction (Bellman et al., 1966; Bezdek and Pal, 1992). Two interesting principles coined by Marr (1982) can be regarded as a useful leitmotiv emphasizing the general role of fuzzy sets in PR, see also (Keller, 1995). These are the principle of least commitment (Don't do something that may later have to be undone) and the principle of graceful degradation (Degrading the data will not prevent the delivery of at least some of the answer). The quantification of these crucial principles can be conveniently expressed by fuzzy sets being utilized in the formulation of the problem and used afterwards in the ensuing classification activities. This special issue highlights some ongoing research in the realm of Fuzzy Pattern Recognition. The material is kept well-balanced and reports on conceptual as well as applied studies. Elsevier Science B.V, PII S 0 1 6 7 - 8 6 5 5 ( 9 6 ) 0 0 0 1 9 - 0
The idea of fuzzy integrals in pattern classifiers, especially in the realm of feature analysis is studied by Grabisch. This contribution is followed by the paper authored by Gader et al. utilizing the concepts of fuzzy integrals in fusion of classifiers in the domain of handwriting recognition. The way in which fuzzy data are derived from grey-tone images is discussed by Bandemer. The two subsequent papers look into the technology of fuzzy sets as an important vehicle of knowledge acquisition and knowledge representation. Baldwin discusses a concept of fuzzy data browsing while Nakagawa et al. elaborate on fuzzy knowledge bases for understanding dynamic images. Fuzzy clustering, especially C-means, has emerged as a successful technique of unsupervised classification. This area is vigorously pursued in the current research and is also reflected in this issue. Hathaway et al. consider a relational data version of some standard fuzzy clustering algorithms; as opposed to object data, a relational character of patterns implies their description in terms of their pairwise similarities. The validation aspects of C-shells clustering are discussed by Dave. The paper by Pedrycz proposes a certain conditioning of fuzzy clustering and studies its usage in data mining. The issue of noise and outlier sensitivity in the setting of robust clustering is addressed by Kim et al. Sequential fuzzy clustering is applied to image analysis by supporting mechanisms of extracting straight lines as discussed by Tsuda et al. Another interesting application of fuzzy C-means to a wastewater treatment process is studied by Marsili-Libelli and Muller. The
566
W. Pedrycz / Pattern Recognition Letters 17 (1996) 565-566
paper by Bortolan et al. discusses hybrid fuzzy-neuro classifiers in the problems of classification of ECG signals. The authors and reviewers deserve our sincere thanks for their effort of making recent achievements in fuzzy sets available to the PR community. Special thanks go the Editor-in-Chief, Edzard Gelsema, for his continuous encouragement during this project. I hope that the readers will find the papers highly informative, innovative and thought-provoking.
References Bellman, R., R. Kalaba and L. Zadeh (1966). Abstraction and pattern classification. J. Math. Anal. AppL 13, 1-7. Bezdek, J.C. and S.K. Pal, Eds., Fuzzy Models for Pattern Recognition. IEEE Press, New York. Keller, J.M. (1995). Fuzzy sets in computer vision. Proc. VI IFSA World Congress, Sao Panlo, Brazil, Vol. I, 7-10. Marr, D. (1982). Vision. W.H. Freeman, San Francisco, CA.