Binary k-nearest neighbor for text categorization
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 des...
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| Published in | Online information review Vol. 29; no. 4; pp. 391 - 399 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Bradford
Emerald Group Publishing Limited
01.01.2005
Emerald |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1468-4527 1468-4535 |
| DOI | 10.1108/14684520510617839 |
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| 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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| AbstractList | 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. 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. 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. (Original abstract) 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. The paper describes the traditional k-nearest neighbor (KNN) classifier, introduces BKNN and outlines experiemental results. The experimental results indicate that BKNN requires much less CPU time than KNN, without loss of classification performance. 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. |
| Author | Tan, Songbo |
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| Cites_doi | 10.1145/312624.312647 10.1145/243199.243277 10.1007/s100440050003 10.1023/A:1009982220290 10.1016/0167-8655(94)90095-7 |
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| Title | Binary k-nearest neighbor for text categorization |
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