A Novel Algorithm for Associative Classification

Authors:
Gourab Kundu;Sirajum Munir;Md. Faizul Bari;Md. Monirul Islam;Kazuyuki Murase
Affiliations:
Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh 1000;Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh 1000;Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh 1000;Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh 1000 and Department of Human and Artificial Intelligence Systems, Graduate Sc ...;Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, Fukui, Japan 910-8507 and Research and Education Program for Life Science, University ...
Venue:
Neural Information Processing
Year:
2008

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Abstract

Associative classifiers have been the subject of intense research for the last few years. Experiments have shown that they generally result in higher accuracy than decision tree classifiers. In this paper, we introduce a novel algorithm for associative classification "Classification based on Association Rules Generated in a Bidirectional Apporach" (CARGBA). It generates rules in two steps. At first, it generates a set of high confidence rules of smaller length with support pruning and then augments this set with some high confidence rules of higher length with support below minimum support. Experiments on 6 datasets show that our approach achieves better accuracy than other state-of-the-art associative classification algorithms.