Lazy Associative Classification

Decision tree classifiers perform a greedy search for rules by heuristically selecting the most promising features. Such greedy (local) search may discard important rules. Associative classifiers, on the other hand, perform a global search for rules satisfying some quality constraints (i.e., minimum support). This global search, however, may generate a large number of rules. Further, many of these rules may be useless during classification, and worst, important rules may never be mined. Lazy (non-eager) associative classification overcomes this problem by focusing on the features of the given test instance, increasing the chance of generating more rules that are useful for classifying the test instance. In this paper we assess the performance of lazy associative classification. First we demonstrate that an associative classifier performs no worse than the corresponding decision tree classifier. Also we demonstrate that lazy classifiers outperform the corresponding eager ones. Our claims are empirically confirmed by an extensive set of experimental results. We show that our proposed lazy associative classifier is responsible for an error rate reduction of approximately 10% when compared against its eager counterpart, and for a reduction of 20% when compared against a decision tree classifier. A simple caching mechanism makes lazy associative classification fast, and thus improvements in the execution time are also observed.