lazy and hybrid algorithms for multi-criteria associative classification

Abstract. Classification aims to map a data instance to its appropriate class (or label). In associative classification the mapping is done through an associa-tion rule with the consequent restricted to the class attribute. Eager associative classification algorithms build a single rule set during the training phase, and this rule set is used to classify all test instances. Lazy algorithms, however, do not build a rule set during the training phase, the rule set generation is delayed until a test instance is given. The choice between eager and lazy algorithms is not simple. Using a single rule set to perform all the predictions may not take advantage of specific characteristics of a test instance. On the other hand, building a specific rule set for each test instance may incur in excessive com-putational efforts. In this paper we propose new eager and lazy algorithms for associative classification. Also, we present heuristics and caching mechanisms to alleviate computational costs during lazy classification, and a new hybrid al-gorithm which combines characteristics of eager and lazy approaches. Finally, we also propose a multi-criteria rule selection technique which is motivated by the intuition that a single criterion cannot give a global picture of the rule (i.e., rules with high confidence but low support). We performed a systematic evalua-tion of the proposed approaches using real data from an important application: spam detection. 1