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Combining Multiple Sets of Rules for Improving Classification Via Measuring Their Closenesses

Bi, Yaxin, Wu, Shengli, Huang, Xuming and Guo, Gongde (2006) Combining Multiple Sets of Rules for Improving Classification Via Measuring Their Closenesses. In: PRICAI 2006: Trends in Artificial Intelligence Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 1068-1072. ISBN 978-3-540-36667-6 [Book section]

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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.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25509
Deposited By: Dr Yaxin Bi
Deposited On:21 Jan 2016 16:29
Last Modified:21 Jan 2016 16:29

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