Heuristic approaches for optimizing the performance of rule-based classifiers

Rule-based classifiers are supervised learning techniques that are extensively used in various domains. This type of classifiers is popular because of its nature which makes it modular and easy to interpret and also because of its ability to provide the classification label as well as the reason behind it. Rule-based classifiers suffer from a degradation of their accuracy when they are used on new data. In this paper, we present an approach that optimizes the performance of the rule-based classifiers on the testing set. The approach is implemented using five different heuristics. We compare the behavior on different data sets that are extracted from different domains. Favorable results are reported.