T-generable indistinguishability operators and their use for feature selection and classification

Abstract T-generable indistinguishability operators are operators E that can be expressed in the form E = T ( E μ 1 , E μ 2 , . . . , E μ m ) , where T is a t-norm and E μ is the fuzzy relation generated by the fuzzy subset μ. In this paper we analyse their relation with powers with respect to the t-norm T and with quasi-arithmetic means. For non-strict continuous Archimedean t-norms they are completely characterised as generable by crisp equivalence relations. These fuzzy relations are used to define a method, called JADE , useful for feature selection and classification tasks. JADE is based on minimising the distance between two indistinguishability measures: the one given by weighting the attribute-values describing the domain objects and the other one given by the correct classification taken as an equivalence relation. The preliminary experiments we carried out with JADE are promising concerning the accuracy in solving classification tasks. We also report some issues of the method that could be improved in the future.

[1]  Elbert A. Walker,et al.  Powers of t-norms , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[2]  Jordi Recasens,et al.  Powers with Respect to t-Norms and t-Conorms and Aggregation Functions , 2016, Fuzzy Logic and Information Fusion.

[3]  Enric Trillas,et al.  On the aggregation of some classes of fuzzy relations , 2002 .

[4]  L. Valverde,et al.  An Inquiry into Indistinguishability Operators , 1984 .

[5]  Jordi Recasens,et al.  Aggregation of T‐transitive relations , 2003, Int. J. Intell. Syst..

[6]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[7]  Adolfo R. de Soto,et al.  Some sets of indistinguishability operators as multiresolution families , 2015, Inf. Sci..

[8]  Jordi Recasens,et al.  Aggregation operators based on indistinguishability operators , 2006, Int. J. Intell. Syst..

[9]  Joan Torrens,et al.  Fuzzy implication functions based on powers of continuous t-norms , 2017, Int. J. Approx. Reason..

[10]  Frans Coenen,et al.  An Attribute Weight Setting Method for k-NN Based Binary Classification using Quadratic Programming , 2002, ECAI.

[11]  Charles Elkan,et al.  Quadratic Programming Feature Selection , 2010, J. Mach. Learn. Res..

[12]  Belur V. Dasarathy,et al.  Data mining tasks and methods: Classification: nearest-neighbor approaches , 2002 .

[13]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[14]  Lotfi A. Zadeh,et al.  Similarity relations and fuzzy orderings , 1971, Inf. Sci..

[15]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[16]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[17]  L. Valverde On the structure of F-indistinguishability operators , 1985 .