Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34670
Title: A rough set-based case-based reasoner for text categorization
Authors: Li, Y
Shiu, SCK 
Pal, SK
Liu, JNK
Keywords: Text categorization (TC)
Case-based reasoning (CBR)
Rough set
Case coverage
Case reachability
Issue Date: 2006
Publisher: Elsevier
Source: International journal of approximate reasoning, 2006, v. 41, no. 2, p. 229-255 How to cite?
Journal: International journal of approximate reasoning
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.
URI: http://hdl.handle.net/10397/34670
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2005.06.019
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