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Text Categorization with ILA

  • Hayri Sever
  • Abdulkadir Gorur
  • Mehmet R. Tolun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2869)

Abstract

The sudden expansion of the web and the use of the internet has caused some research fields to regain (or even increase) its old popularity. Of them, text categorization aims at developing a classification system for assigning a number of predefined topic codes to the documents based on the knowledge accumulated in the training process. We propose a framework based on an automatic inductive classifier, called ILA, for text categorization, though this attempt is not a novel approach to the information retrieval community. Our motivation are two folds. One is that there is still much to do for efficient and effective classifiers. The second is of ILA’s (Inductive Learning Algorithm) well-known ability in capturing by canonical rules the distinctive features of text categories. Our results with respect to the Reuters 21578 corpus indicate (1) the reduction of features by information gain measurement down to 20 is essentially as good as the case where one would have more features; (2) recall/precision breakeven points of our algorithm without tuning over top 10 categories are comparable to other text categorization methods, namely similarity based matching, naive Bayes, Bayes nets, decision trees, linear support vector machines, steepest descent algorithm.

Keywords

Text Categorization Inductive Learning Feature Selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hayri Sever
    • 1
  • Abdulkadir Gorur
    • 2
  • Mehmet R. Tolun
    • 3
  1. 1.Department of Computer EngineeringBaskent UniversityAnkaraTurkey
  2. 2.Department of Computer EngineeringEastern Mediterranean UniversityFamagusta, T.R.N.C.Turkey
  3. 3.Department of Computer EngineeringAtilim UniversityAnkaraTurkey

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