Improving Classification Decisions by Multiple Knowledge

Yaxin Bi, Sally I. McClean, Anderson Terry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

An important issue in data mining is how to make use of multiple discovered knowledge to improve future decisions. In this paper, we propose a new approach to combining multiple sets of rules for text categorization using Dempster's rule of combination. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model classification decisions from multiple sets of rules as pieces of evidence which can be combined by Dempster's rule of combination. We apply these methods to 10 out of the 20-newsgroups - a benchmark data collection, individually and in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on the 10 groups of the benchmark data is statistically significantly better than that of the best single set of rules. The comparative analysis between the Dempster-Shafer and the majority voting methods along with an overfitting study confirm the advantage and the robustness of our approach
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Number of pages8
Publication statusPublished - 2005
EventTools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on -
Duration: 1 Jan 2005 → …

Conference

ConferenceTools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on
Period1/01/05 → …

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  • Cite this

    Bi, Y., McClean, S. I., & Terry, A. (2005). Improving Classification Decisions by Multiple Knowledge. In Unknown Host Publication IEEE.