Detalles de Evento

Título: Promoting Explainability in Fuzzy Systems via Hierarchical Fuzzy Exception Rules.
Impartida por: José Ramón Trillo Vílchez.
Fecha: 19 de Mayo de 2021 de 12:00 a 13:00.
Resumen: When developing a Machine Learning model, the consideration of explainability as an additional design driver can improve its deployment into any application context. Given an audience, an eXplainable Artificial Intelligence system produces details or reasons to make it is functioning clear or easy to understand. Among different paradigms that inherently support these capabilities, Fuzzy Rule-Based Systems are a very accountable solution. When dealing with fuzzy systems, the main issue is to select an appropriate granularity to represent (fuzzify) the input data. A low value may cause the generation of too generalist rules, causing a hinder on predictive performance, whereas a high value may lead to both overfitting and/or very complex solutions. To overcome this situation, we propose a novel hierarchical fuzzy classification system based on fuzzy exception rules. To do so, low granularity rules are first generated and their confidence is examined. For those cases in which the fuzzy confidence is below a quality threshold, new higher granularity rules are created to cover the “instances in conflict” for the general rule, which is still kept in the rule base. Experimental results show the achievement of a compact and interpretable final rule base while maintaining or improving the predictive performance in comparison with the baseline fuzzy rule-based classification and hierarchical systems.
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