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An kNN Model-Based Approach and Its Application in Text Categorization. CICLing 2004: 559-570

Guo, Gongde, Wang, Hui, Bell, David A., Bi, Yaxin and Greer, Kieran (2004) An kNN Model-Based Approach and Its Application in Text Categorization. CICLing 2004: 559-570. In: Computational Linguistics and Intelligent Text Processing Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 559-570. ISBN 978-3-540-21006-1 [Book section]

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Abstract

An investigation has been conducted on two well known similarity-based learning approaches to text categorization. This includes the k-nearest neighbor (k-NN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, we propose a new classifier called the kNN model-based classifier by unifying the strengths of k-NN and Rocchio classifier and adapting to characteristics of text categorization problems.A text categorization prototypes system has been implemented and then evaluated on two common document corpora, namely, the 20-newsgroup collection and the ModApte version of the Reuters-21578 collection of news stories. The experimental results show that the kNN model-based approach outperforms the k-NN, Rocchio classifier.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25515
Deposited By: Dr Yaxin Bi
Deposited On:21 Jan 2016 16:30
Last Modified:21 Jan 2016 16:30

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