Associative classification based on the Transferable Belief Model

摘要

Associative classification is, in essence, a frequent itemset mining technique designed specifically for classification tasks. It can be viewed as a second-order data mining problem, where the aim is to obtain an accurate classifier taking the mined set formed by class association rules (CARs) as raw material; that is, association rules in which only the class attribute is considered in the rule’s consequent. Traditionally, the classifier is obtained from the initial CAR−set applying heuristic methods for sorting and pruning, leading to a (relatively small) set of classification rules. In this paper, instead of using heuristic-based methods, we propose the use of the Transferable Belief Model for obtaining an understandable, accurate and compact classifier composed of (pignistic) probability functions that summarize the huge mined set of rules. The experimental results obtained with benchmark datasets show the effectiveness of our promising proposal.