Combining multiple sets of rules for improving classification via measuring their closenesses

Authors:
Yaxin Bi;Shengli Wu;Xuming Huang;Gongde Guo
Affiliations:
School of Computing and Mathematics, University of Ulster, Newtownabbey, UK;School of Computing and Mathematics, University of Ulster, Newtownabbey, UK;Dept. of Computing, Univ. of Bradford, Bradford, UK and Dept. of Computer Science, Fujian Normal Univ., Fuzhou, China;Dept. of Computing, Univ. of Bradford, Bradford, UK and Dept. of Computer Science, Fujian Normal Univ., Fuzhou, China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

In this paper, we propose a new method for measuring the closeness of multiple sets of rules that are combined using Dempster's rule of combination to improve classification performance. The closeness provides an insight into combining multiple sets of rules in classification - in what circumambience the performance of combinations of some sets of rules using Dempster's rule is better than that of others. Experiments have been carried out over the 20-newsgroups benchmark data collection, and the empirical results show that when the closeness between two sets of rules is higher than that of others, the performance of its combination using Dempster's rule is better than the others.