A rough set-based case-based reasoner for text categorization

Y. Li, Chi Keung Simon Shiu, S. K. Pal, J. N.K. Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

This paper presents a novel rough set-based case-based reasoner for use in text categorization (TC). The reasoner has four main components: feature term extractor, document representor, case selector, and case retriever. It operates by first reducing the number of feature terms in the documents using the rough set technique. Then, the number of documents is reduced using a new document selection approach based on the case-based reasoning (CBR) concepts of coverage and reachability. As a result, both the number of feature terms and documents are reduced with only minimal loss of information. Finally, this smaller set of documents with fewer feature terms is used in TC. The proposed rough set-based case-based reasoner was tested on the Reuters21578 text datasets. The experimental results demonstrate its effectiveness and efficiency as it significantly reduced feature terms and documents, important for improving the efficiency of TC, while preserving and even improving classification accuracy.
Original languageEnglish
Pages (from-to)229-255
Number of pages27
JournalInternational Journal of Approximate Reasoning
Volume41
Issue number2
DOIs
Publication statusPublished - 1 Feb 2006

Keywords

  • Case coverage
  • Case reachability
  • Case-based reasoning (CBR)
  • Rough set
  • Text categorization (TC)

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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