Exploiting statistically significant dependent rules for associative classification

Established associative classification algorithms have shown to be very effective in handling categorical data such as text data. The learned model is a set of rules that are easy to understand and can be edited. However, they still suffer from the following limitations: first, they mostly use the support-confidence framework to mine classification association rules which require the setting of some confounding parameters; second, the lack of statistical dependency in the used framework may lead to the omission of many interesting rules and the detection of meaningless rules; third, the rule generation process usually generates a sheer number of rules which puts in question the interpretability and readability of the learned associative classification model. In this paper, we propose a novel associative classifier, SigDirect, to address the above problems. In particular, we use Fisher’s exact test as a significance measure to directly mine classification association rules by some effective pruning strategies. Without any threshold settings like minimum support and minimum confidence, SigDirect is able to find nonredundant classification association rules which express a statistically significant dependency between a set of antecedent items and a consequent class label. To further reduce the number of noisy rules, we present an instance-centric rule pruning strategy to find a subset of rules of high quality. At last, we propose and investigate various rule classification strategies to achieve a more accurate classification model. Experimental results The work was done when the author was at University of Alberta