Ranking Rules in Associative Classifiers via Borda's Methods

Abstract

Associative classifiers have been widely used in many domains due to their inherent interpretability. They are built in steps, one of them aimed at ranking the rules, usually performed through objective measures. Works aim to modify this step in order to obtain a classifier with better performance. Among them are those that use multiple measures simultaneously in order to consider different points of view for a given rule. However, these works present problems regarding execution time and interpretability. Here we show the use of ranking aggregation methods, specifically Borda's methods, to rank the rules through a set of measures. Our results demonstrate that our solution is fast to execute and still guarantee the interpretability of the models, since they contain a statistically significant smaller number of rules.