Feature Selection Methods for an Improved SVM Classifier

Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, three feature selection methods are evaluated: Random Selection, Information Gain (IG) and Support Vector Machine feature selection (called SVM_FS). We show that the best results were obtained with SVM_FS method for a relatively small dimension of the feature vector. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Authors:

• Daniel Morariu
• Lucian N. Vintan
• Volker Tresp

Keywords:

• Feature Selection
• Learning with Kernels
• SupportVector Machine
• and Classification.

References:

[1] S. Chakrabarti, "Mining the Web- Discovering Knowledge from
hypertext data", Morgan Kaufmann Press, 2003.
[2] G. Forman, "A Pitfall and Solution in Multi-Class Feature Selection for
Text Classification", Proceedings of the 21st International Conference
on Machine Learning, Banff, Canada, 2004.
[3] T. Jebara, "Multi Task Feature and Kernel Selection for SVMs",
Proceedings of the 21st International Conference on Machine Learning,
Banff, Canada, 2004.
[4] T. Mitchell, "Machine Learning", McGraw Hill Publishers, 1997.
[5] D. Mladenic, J. Brank, M. Grobelnik and N. Milic-Frayling, "Feature
Selection Using Support Vector Machines", The 27th Annual
International ACM SIGIR Conference (SIGIR2004), pp 234-241, 2004.
[6] D. Morariu, "Classification and Clustering using Support Vector
Machine", 2nd PhD Report, University ÔÇ×Lucian Blaga" of Sibiu,
September, 2005, http://webspace.ulbsibiu.ro/ daniel.morariu/html/Docs
/Report2.pdf.
[7] D. Morariu, L. Vintan, "A Better Correlation of the SVM kernel-s
Parameters", Proceeding of The 5th RoEduNet International Conference,
Sibiu, June 2006.
[8] C. Nello, J. Swawe-Taylor, "An introduction to Support Vector
Machines", Cambridge University Press, 2000.
[9] J. Platt, "Fast training of support vector machines using sequential
minimal optimization". In B. Scholkopf, C. J. C. Burges, and A. J.
Smola, editors, Advances in Kernel Methods - Support Vector Learning,
pages 185-208, Cambridge, MA, 1999, MIT Press.
[10] Reuters Corpus: http://about.reuters.com/researchandstandards/corpus/.
Released in November 2000.
[11] B. Schoelkopf, A. Smola, "Learning with Kernels, Support Vector
Machines", MIT Press, London, 2002.