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Online Information Review
ISSN: 1468-4527
Online from: 1977
Subject Area: Library and Information Studies
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Binary k-nearest neighbor for text categorization
| Title: | Binary k-nearest neighbor for text categorization |
|---|---|
| Author(s): | Songbo Tan, (Software Department, Institute of Computing Technology, Chinese Academy of Sciences, People's Republic of China) |
| Citation: | Songbo Tan, (2005) "Binary k-nearest neighbor for text categorization", Online Information Review, Vol. 29 Iss: 4, pp.391 - 399 |
| Keywords: | Classification, Data handling, Information retrieval |
| Article type: | General review |
| DOI: | 10.1108/14684520510617839 (Permanent URL) |
| Publisher: | Emerald Group Publishing Limited |
| Abstract: | Purpose – With the ever-increasing volume of text data via the internet, it is important that documents are classified as manageable and easy to understand categories. This paper proposes the use of binary k-nearest neighbour (BKNN) for text categorization. Design/methodology/approach – The paper describes the traditional k-nearest neighbor (KNN) classifier, introduces BKNN and outlines experiemental results. Findings – The experimental results indicate that BKNN requires much less CPU time than KNN, without loss of classification performance. Originality/value – The paper demonstrates how BKNN can be an efficient and effective algorithm for text categorization. Proposes the use of binary k-nearest neighbor (BKNN ) for text categorization. |
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