Computing, Artificial Intelligence and Information Technology Fuzzy classification systems

In this paper it is pointed out that a classification is always made taking into account all the available classes, i.e., by means of a classification system. The approach presented in this paper generalizes the classical definition of fuzzy partition as defined by Ruspini, which is now conceived as a quite often desirable objective that can be usually obtained only after a long learning process. In addition, our model allows the evaluation of the resulting classification, according to several indexes related to covering, relevance and overlapping. � 2003 Elsevier B.V. All rights reserved.

[1]  J. Montero,et al.  Representation of consistent recursive rules , 2001, Eur. J. Oper. Res..

[2]  V. Cutello,et al.  Non deterministic fuzzy classification systems , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[3]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[4]  Vincenzo Cutello,et al.  Recursive connective rules , 1999, Int. J. Intell. Syst..

[5]  D. Butnariu.  Additive fuzzy measures and integrals, III , 1983 .

[6]  Michael Spann,et al.  A new approach to clustering , 1990, Pattern Recognit..

[7]  Dan Dumitrescu,et al.  Fuzzy partitions with the connectives T∞, S∞ , 1992 .

[8]  Bernard De Baets,et al.  Idempotent uninorms , 1999, Eur. J. Oper. Res..

[9]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decision-making , 1988 .

[10]  J. Montero,et al.  On the principles of fuzzy classification , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[11]  G. M. Foody.  The Continuum of Classification Fuzziness in Thematic Mapping , 1999 .

[12]  J. Montero,et al.  CLASSIFYING PIXELS BY MEANS OF FUZZY RELATIONS , 2000 .

[13]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[14]  F. J. Juan,et al.  Extensive fuzziness , 1987 .

[15]  H. Thiele,et al.  A characterization of arbitrary Ruspini partitions by fuzzy similarity relations , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[16]  Marc Roubens,et al.  Fuzzy Preference Modelling and Multicriteria Decision Support , 1994, Theory and Decision Library.

[17]  F J Montero de Juan.  Comprehensive fuzziness , 1986 .

[18]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[19]  Javier Montero,et al.  Spectral fuzzy classification: an application , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[20]  J. Bezdek,et al.  Fuzzy partitions and relations; an axiomatic basis for clustering , 1978 .

[21]  J. Montero,et al.  A general model for deriving preference structures from data , 1997 .

[22]  Ronald R. Yager,et al.  Uninorm aggregation operators , 1996, Fuzzy Sets Syst..

[23]  Bernard Roy,et al.  Decision science or decision-aid science? , 1993 .

[24]  Valerie Belton,et al.  Facilitators, decision makers, D.I.Y. users: Is intelligent multicriteria decision support for all feasible or desirable? , 1999, Eur. J. Oper. Res..

[25]  M. Roubens.  Pattern classification problems and fuzzy sets , 1978 .

[26]  J. Dombi.  Basic concepts for a theory of evaluation: The aggregative operator , 1982 .

[27]  Pascal Matsakis,et al.  Evaluation of Fuzzy Partitions , 2000 .

[28]  J. Fodor,et al.  Valued preference structures , 1994 .

[29]  J. Dombi.  A general class of fuzzy operators, the demorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators , 1982 .

[30]  Radko Mesiar,et al.  New construction methods for aggregation operators. , 2000 .

[31]  Ion Iancu,et al.  Connectives for fuzzy partitions , 1999, Fuzzy Sets Syst..