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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Feature Selection Using Particle Swarm Optimization in Text Categorization

Mehdi Hosseinzadeh Aghdam
  • Department of Computer Engineering and Information Technology, Payame Noor University (PNU)P.O.BOX 193953697, Tehran, IRAN
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Setareh Heidari
Published Online: 2015-10-29 | DOI: https://doi.org/10.1515/jaiscr-2015-0031


Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.


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About the article

Published Online: 2015-10-29

Published in Print: 2015-10-01

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 5, Issue 4, Pages 231–238, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0031.

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© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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