Building a Simple and Effective Text Categorization System using Relative Importance in Category

  • Authors:

  • Bingheng Yan;Depei Qian

  • Affiliations:

  • Xi'an Jiaotong University, China;Xi'an Jiaotong University, China

  • Venue:
  • ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
  • Year:
  • 2007

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Abstract

With the rapid development of World Wide Web, text categorization has become the key technology in organizing and processing large volume of document data. There are a variety of text categorization methods such as k Nearest Neighbor (kNN) and Support Vector Machine (SVM). However, those methods are either too complicated or not effective enough. In this paper, we present a new method called Relative Importance in Category (RIIC), which is simpler than most methods and has a lower time complexity. To verify the performance of RIIC, we build a text categorization system (TCS) based on RIIC and compare our system with TCS based on kNN and SVM. Experimental results show that in most cases the performance of RIIC is better than kNN and SVM.