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.
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Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K. (2004). An kNN Model-Based Approach and Its Application in Text Categorization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_69
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DOI: https://doi.org/10.1007/978-3-540-24630-5_69
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