ACME: An Associative Classifier based on Maximum Entropy

Abstract

Recent studies in classification have proposed ways of exploiting the association rule mining paradigm. These studies have performed extensive experiments to show their techniques to be both efficient and accurate. However, they do not provide any theoretical justification or statistical basis for their methods. In this work, we propose a new classifier based on association rule mining. Our classifier rests on the maximum entropy principle for its statistical basis. In particular, we use the classical generalized iterative scaling algorithm (GIS) to create our classification model. We show that GIS fails in some cases when itemsets are used as features and provide modifications to rectify this problem. We also describe techniques to make GIS tractable for large feature spaces -- we provide a new technique to divide a feature space into independent clusters each of which can be handled separately.