Associative Classification With Artificial Immune System

Abstract

Associative classification (AC), which is based on association rules, has shown great promise over many other classification techniques. To implement AC effectively, we need to tackle the problems on the very large search space of candidate rules during the rule discovery process and incorporate the discovered association rules into the classification process. This paper proposes a new approach that we call artificial immune system-associative classification (AIS-AC), which is based on AIS, for mining association rules effectively for classification. Instead of massively searching for all possible association rules, AIS-AC will only find a subset of association rules that are suitable for effective AC in an evolutionary manner. In this paper, we also evaluate the performance of the proposed AIS-AC approach for AC based on large datasets. The performance results have shown that the proposed approach is efficient in dealing with the problem on the complexity of the rule search space, and at the same time, good classification accuracy has been achieved. This is especially important for mining association rules from large datasets in which the search space of rules is huge.