Using kNN Model-based Approach for Automatic Text Categorization

نویسندگان

  • Gongde Guo
  • Hui Wang
  • David Bell
  • Yaxin Bi
  • Kieran Greer
چکیده

An investigation has been conducted on two well known similarity-based learning approaches to text categorization: the k-nearest neighbor (k-NN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, a new classifier called the kNN model-based classifier (kNNModel) has been proposed. It combines the strength of both k-NN and Rocchio. A text categorization prototype system has been presented. It implements kNNModel along with kNN, Rocchio and Support Vector Machine (SVM). An evaluation has been carried out on two common document corpora, namely, the 20newsgroup 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 and Rocchio classifiers, and is comparable to SVM, which is used as a benchmark in our experiments.

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تاریخ انتشار 2003