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Using kNN model for automatic text categorization

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Abstract

An investigation is conducted on two well-known similarity-based learning approaches to text categorization: the k-nearest neighbors (kNN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, a new classifier called the kNN model-based classifier (kNN Model) is proposed. It combines the strength of both kNN and Rocchio. A text categorization prototype, which implements kNN Model along with kNN and Rocchio, is described. An experimental evaluation of different methods is carried out on two common document corpora: the 20-newsgroup collection and the ModApte version of the Reuters-21578 collection of news stories. The experimental results show that the proposed kNN model-based method outperforms the kNN and Rocchio classifiers, and is therefore a good alternative for kNN and Rocchio in some application areas.

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Authors and Affiliations

  1. School of Computing and Mathematics, University of Ulster, (Shore Road, Newtownabbey), Co. Antrim, BT37 0QB, UK

    Gongde Guo, Hui Wang & Kieran Greer

  2. School of Computer Science, Queen's University Belfast, Belfast, BT7 1NN, UK

    David Bell & Yaxin Bi

Authors
  1. Gongde Guo
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  2. Hui Wang
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  3. David Bell
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  4. Yaxin Bi
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  5. Kieran Greer
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Additional information

This work was partly supported by the European Commission project ICONS, project no. IST-2001-32429.

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Guo, G., Wang, H., Bell, D. et al. Using kNN model for automatic text categorization. Soft Comput 10, 423–430 (2006). https://doi.org/10.1007/s00500-005-0503-y

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  • DOI: https://doi.org/10.1007/s00500-005-0503-y

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