Hierarchical Multi-label Associative Classification (HMAC) using negative rules

Abstract

Hierarchical Classification is a very important classification task for arranging data in a hierarchical structure. Hierarchical arrangement of data is one of the best methods to achieve better understanding of complex data. In this paper, we propose the HMAC method to perform Hierarchical Multi-label Associative Classification. This method uses multiple and negative rules to predict class-set and to filter exceptional cases in order to improve both accuracy and explanatory ability of the resulting classifier. Redundant rule pruning method for negative rules and rule ranking method for hierarchical associative classification are developed. Moreover, we propose a rule evaluation measure, Sim, that tread-off between F-measure and Jaccard's coefficient to encode hierarchical structure meaning of actual and predicted node collections. We show that Sim is robust and simple rule evaluation measure. Experimental results show that HMAC improves accuracy and explanatory ability of hierarchical classifiers compared with various rule ranking and pruning techniques.