Publication: Fuzzy classification systems.
Loading...
Full text at PDC
Publication Date
2004
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Science
Citations
Exportar
Abstract
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.
Description
Unesco subjects
Keywords
Citation
A. Amo, J. Montero, V. Cutello, On the principles of fuzzy Information Processing Society Conference, 1999, pp.675–679.
A. Amo, J. Montero, G. Biging, Classifying pixels by means of fuzzy relations, International Journal of General Systems 29 (2000) 605–621.
A. Amo, J. Montero, A. Fernandez, M. Lopez, J. Tordesillas, G. Biging, Spectral fuzzy classification: An application, IEEE Transactions Systems Man and Cybernautics (C) 32 (2002) 42–48.
A. Amo, J. Montero, E. Molina, On the representation of recursive rules, European Journal of Operational Research 130 (2001) 29–53.
G.H. Ball, D.J. Hall, ISODATA––A Novel Method of Data Analysis and Pattern Classification, Stanford Research Institute, Menlo Park, CA, 1965.
V. Belton, J. Hodgkin, Facilitators, decision makers, D.I.Y. users: Is intelligent multicriteria decision support for all feasible or desirable, European Journal of Operational Research 113 (1999) 247–260.
J.C. Bezdek, J.D. Harris, Fuzzy partitions and relations: An axiomatic basis for clustering, Fuzzy Sets and Systems 1 (1978) 111–127.
D. Butnariu, Additive fuzzy measures and integrals, Journal of Mathematical Analysis and Applications 93 (1983) 436–452.
V. Cutello, J. Montero, Nondeterministic fuzzy classification systems, In: Proceedings FUZZ-IEEE Conference, 1997, pp. 1689–1694.
V. Cutello, J. Montero, Recursive connective rules, International of Journal of Intelligent Systems 14 (1999) 3–20.
B. De Baets, Idempotent uninorms, European Journal of Operational Research 118 (1999) 631–642.
J. Dombi, Basic concepts for a theory of evaluation: The aggregative operator, European Journal of Operational Research 10 (1982) 282–293.
J. Dombi, A general class of fuzzy operators, Fuzzy Sets and Systems 8 (1982) 149–163.
D. Dumitrescu, Fuzzy partitions with the connectives T1, S1, Fuzzy Sets and Systems 47 (1992) 193–195.
J. Fodor, M. Roubens, Valued preference structures, European Journal of Operational Research 79 (1994) 277–286.
J. Fodor, M. Roubens, Fuzzy Preference Modelling and Multicriteria Decision Support, Kluwer, Dordrecht, 1994.
G.M. Foody, The continuum of classification fuzziness in thematics mapping, Photogrammetric Engineering and Remote Sensing 65 (1999) 443–451.
I. Iancu, Connectives for fuzzy partitions, Fuzzy Sets and Systems 101 (1999) 509–512.
P. Matsakis, S. Andr_efou€et, P. Capolsini, Evaluation of fuzzy partitions, Remote Sensing of Environment 74 (2000) 516–533.
R. Mesiar, Aggregation operators: Some classes and construction methods, In: Proceedings Eight International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2000, pp. 707–711.
R. Mesiar, B. De Baets, New construction methods for aggregation operators, In: Proceedings Eight International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2000, pp. 701–710.
J. Montero, Comprehensive fuzziness, Fuzzy Sets and Systems 20 (1986) 86–89.
J. Montero, Extensive fuzziness, Fuzzy Sets and Systems 21 (1986) 201–209.
J. Montero, J. Tejada, V. Cutello, A general model for deriving preference structures from data, European Journal of Operational Research 98 (1997) 98–110.
J. Montero, J. Y_a~nez, J. Gonz_alez-Pach_on, Searching for the dimension of a fuzzy preference relation, EURO Conference Budapest, July 16–19, 2000.
M. Roubens, Pattern classification problems and fuzzy sets, Fuzzy Sets and Systems 1 (1978) 239–253.
B. Roy, Decision science or decision-aid science, European Journal of Operational Research 66 (1993) 184–203.
E.H. Ruspini, A new approach to clustering, Information and Control 15 (1969) 22–32.
B. Schwizer, A. Sklar, Probabilistic Metric Spaces, North-Holland, New York, 1983.
G. Shafer, Savage revisited, Statistical Science 1 (1990) 463–501.
H. Thiele, A characterization of Ruspini-partitions by similarity relations, In: Proceedings International Conference on Information Processing and Management of
Uncertainty in Knowledge-Based Systems, 1996, pp. 389–394.
H. Thiele, A characterization of arbitrary Ruspini-partitions by fuzzy similarity relations, In: Proceedings FUZZIEEE Conference, 1997, pp. 131–134.
E. Trillas, On negation functions in fuzzy set theory, in: S.Barro, A. Bugarın, A. Sobrino (Eds.), Advances in Fuzzy logic, Universidade de Santiago de Compostela, 1998, pp. 31–43.
R.R. Yager, On ordered weighted averaging aggregation operators in multi-criteria decision making, IEEE Transactions on Systems, Man and Cybernetics 18 (1988) 183–190.
R. Yager, A. Rybalov, Uninorm aggregation operators, Fuzzy Sets and Systems 80 (1996) 111–120.
J. Yañez, J. Montero, A poset dimension algorihtm, Journal of Algorithms 30 (1999) 185–208.
L.A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338–353.
L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man and Cybernetics 1 (1973) 28–44.