Fuzzy rule-based systems are raising great interest in the last years in eXpalianable Artificial Intelligence. These systems represents knowledge easily understood by humans but they are not interpretable per se. They, in fact, must remain simple and understandable, and the rule base must be compactness. In this work a fuzzy rule base minimization approach based on rough sets theory and a greedy algorithm is proposed. The reduction of the fuzzy rules makes the rule base simpler, and thus easier to produce explainable inference systems (e.g., decision support systems and recommenders). Encouraging results are obtained validating and comparing the methodology on data of UCI benchmark.
A Fuzzy Rule Base Minimization Perspective in XAI
Camastra F.;Ciaramella A.
;Staiano A.
2021-01-01
Abstract
Fuzzy rule-based systems are raising great interest in the last years in eXpalianable Artificial Intelligence. These systems represents knowledge easily understood by humans but they are not interpretable per se. They, in fact, must remain simple and understandable, and the rule base must be compactness. In this work a fuzzy rule base minimization approach based on rough sets theory and a greedy algorithm is proposed. The reduction of the fuzzy rules makes the rule base simpler, and thus easier to produce explainable inference systems (e.g., decision support systems and recommenders). Encouraging results are obtained validating and comparing the methodology on data of UCI benchmark.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
