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

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PRICAI 2006: Trends in Artificial Intelligence (PRICAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4099))

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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.

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© 2006 Springer-Verlag Berlin Heidelberg

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Bi, Y., Wu, S., Huang, X., Guo, G. (2006). Combining Multiple Sets of Rules for Improving Classification Via Measuring Their Closenesses. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_135

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  • DOI: https://doi.org/10.1007/978-3-540-36668-3_135

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36667-6

  • Online ISBN: 978-3-540-36668-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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