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.
|Title of host publication||Computational Linguistics and Intelligent Text Processing Lecture Notes in Computer Science|
|Publication status||Published - 2004|
Guo, G., Wang, H., Bell, D. A., Bi, Y., & Greer, K. (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 (pp. 559-570). Springer.