Finding Simple Fuzzy Classification Systems with High Interpretability Through Multiobjective Rule Selection

In this paper, we demonstrate that simple fuzzy rule-based classification systems with high interpretability are obtained through multiobjective genetic rule selection. In our approach, first a prespecified number of candidate fuzzy rules are extracted from numerical data in a heuristic manner using rule evaluation criteria. Then multiobjective genetic rule selection is applied to the extracted candidate fuzzy rules to find a number of non-dominated rule sets with respect to the classification accuracy and the complexity. The obtained non-dominated rule sets form an accuracy-complexity tradeoff surface. The performance of each non-dominated rule set is evaluated in terms of its classification accuracy and its complexity. Computational experiments show that our approach finds simple fuzzy rules with high interpretability for some benchmark data sets in the UC Irvine machine learning repository.