Multiple labels associative classification

Authors:
Abdeljaber Thabtah;Peter Cowling;Yonghong Peng
Affiliations:
Department of Computing, MOSAIC Research Centre, School of Informatics, University of Bradford, BD7 1DP, Bradford, UK;Department of Computing, MOSAIC Research Centre, School of Informatics, University of Bradford, BD7 1DP, Bradford, UK;Department of Computing, School of Informatics, University of Bradford, BD7 1DP, Bradford, UK
Venue:
Knowledge and Information Systems
Year:
2006

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Abstract

Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multi-label associative classification approach (MMAC). In addition, four measures are presented in this paper for evaluating the accuracy of classification approaches to a wide range of traditional and multi-label classification problems. Results for 19 different data sets from the UCI data collection and nine hyperheuristic scheduling runs show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches.