Associative Classification with Statistically Significant Positive and Negative Rules

Rule-based classifier has shown its popularity in building many decision support systems such as medical diagnosis and financial fraud detection. One major advantage is that the models are human understandable and can be edited. Associative classifiers, as an extension of rule-based classifiers, use association rules to associate attributes with class labels. A delicate issue of associative classifiers is the need for subtle thresholds: minimum support and minimum confidence. Without prior knowledge, it could be difficult to choose the proper thresholds, and the discovered rules within the support-confidence framework are not statistically significant, i.e., inclusion of noisy rules and exclusion of valuable rules. Besides, most associative classifiers proposed so far, are built with only positive association rules. Negative rules, however, are also able to provide valuable information to discriminate between classes. To solve the above mentioned problems, we propose a novel associative classifier which is built upon both positive and negative classification association rules that show statistically significant dependencies. Experimental results on real-world datasets show that our method achieves competitive or even better performance than well-known rule-based and associative classifiers in terms of both classification accuracy and computational efficiency.