RT info:eu-repo/semantics/article
T1 Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection
A1 Galende Hernández, Marta
A1 Sáinz Palmero, Gregorio Ismael
A1 Fuente Aparicio, María Jesús de la
K1 Fuzzy modeling
K1 Accuracy
K1 Interpretability
K1 Complexity
K1 Genetic algorithms
AB The aim of this paper is to develop a general post-processing methodology to reduce the complexity of data-driven linguistic fuzzy models, in order to reach simpler fuzzy models preserving enough accuracy and better fuzzy linguistic performance with respect to their initial values. This post-processing approach is based on rule selection via the formulation of a bi-objective problem with one objective focusing on accuracy and the other on interpretability. The latter is defined via the aggregation of several interpretability measures, based on the concepts of similarity and complexity of fuzzy systems and rules. In this way, a measure of the fuzzy model interpretability is given. Two neuro-fuzzy systems for providing initial fuzzy models, Fuzzy Adaptive System ART based and Neuro-Fuzzy Function Approximation and several case studies, data sets from KEEL Project Repository, are used to check this approach. Both fuzzy and neuro-fuzzy systems generate Mamdani-type fuzzy rule-based systems, each with its own particularities and complexities from the point of view of the fuzzy sets and the rule generation. Based on these systems and data sets, several fuzzy models are generated to check the performance of the proposal under different restrictions of complexity and fuzziness.
PB Springer Nature
SN 1432-7643
YR 2012
FD 2012
LK https://uvadoc.uva.es/handle/10324/74370
UL https://uvadoc.uva.es/handle/10324/74370
LA eng
NO Soft Comput 16, 451–470 (2012)
NO Producción Científica
DS UVaDOC
RD 04-abr-2025