TY - GEN

T1 - A characterization of linearly compensated hybrid connectives used in fuzzy classifications

AU - Sańchez, Mónica

AU - Prats, Francesc

AU - Agell, Núria

AU - Aguilar-Martin, Joseph

PY - 2004

Y1 - 2004

N2 - The study of linearly compensated hybrid connectives H = C+(1¡) C, where C is a t-norm and C represents the dual connective of C, to define aggregation operators for fuzzy classifications is a key point not only in fuzzy sets theory but also in learning processes. Although these operators are not associative, the fact that they can be decomposed into associative functions easily gives rise to n-Ary aggregation functions by straightforward iteration. Among the most commonly used t-norms are those of Frank's family, which are simultaneously t-norms and copulas. The purpose of this paper is to give a characterization of the hybrid connective H, via the properties of the connective C. Necessary and sufficient conditions of H that define C as a copula are given. The characterized hybrid connectives H are used to compute the global adequacy degree of an object in a class from marginal adequacy degrees in a learning system.

AB - The study of linearly compensated hybrid connectives H = C+(1¡) C, where C is a t-norm and C represents the dual connective of C, to define aggregation operators for fuzzy classifications is a key point not only in fuzzy sets theory but also in learning processes. Although these operators are not associative, the fact that they can be decomposed into associative functions easily gives rise to n-Ary aggregation functions by straightforward iteration. Among the most commonly used t-norms are those of Frank's family, which are simultaneously t-norms and copulas. The purpose of this paper is to give a characterization of the hybrid connective H, via the properties of the connective C. Necessary and sufficient conditions of H that define C as a copula are given. The characterized hybrid connectives H are used to compute the global adequacy degree of an object in a class from marginal adequacy degrees in a learning system.

KW - Classification algorithms

KW - Hybrid connectives

KW - Machine learning

KW - Qualitative reasoning

KW - Reasoning under uncertainty

UR - http://www.scopus.com/inward/record.url?scp=84914176872&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84914176872

T3 - Frontiers in Artificial Intelligence and Applications

SP - 1081

EP - 1082

BT - ECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings

A2 - de Mantaras, Ramon Lopez

A2 - Saitta, Lorenza

PB - IOS Press

T2 - 16th European Conference on Artificial Intelligence, ECAI 2004

Y2 - 22 August 2004 through 27 August 2004

ER -