An Evolutionary Approach for Ruleset Selection in a Class Based Associative Classifier

Associative classification is an emerging technique to build an accurate classifier that integrates two popular data mining techniques namely association rule mining and classification. Recent studies and experimental results prove that associative classification achieves higher accuracy than traditional classification approaches. However, it is a known fact that associative classification typically yields a large number of rules, from which a set of high quality rules are chosen to construct an efficient classifier. Hence, ranking and selecting a small subset of high-quality rules without jeopardizing the classification accuracy is of prime importance but a challenging task indeed.