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A study on mutual information-based feature selection for text categorization

Xu , Yang and Jones, Gareth J.F. and Li, JinTao and Wang, Bin and Sun, ChunMing (2007) A study on mutual information-based feature selection for text categorization. Journal of Computational Information Systems, 3 (3). pp. 1007-1012. ISSN 1553-9105

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Feature selection plays an important role in text categorization. Automatic feature selection methods such as document frequency thresholding (DF), information gain (IG), mutual information (MI), and so on are commonly applied in text categorization. Many existing experiments show IG is one of the most effective methods, by contrast, MI has been demonstrated to have relatively poor performance. According to one existing MI method, the mutual information of a category c and a term t can be negative, which is in conflict with the definition of MI derived from information theory where it is always non-negative. We show that the form of MI used in TC is not derived correctly from information theory. There are two different MI based feature selection criteria which are referred to as MI in the TC literature. Actually, one of them should correctly be termed "pointwise mutual information" (PMI). In this paper, we clarify the terminological confusion surrounding the notion of "mutual information" in TC, and detail an MI method derived correctly from information theory. Experiments with the Reuters-21578 collection and OHSUMED collection show that the corrected MI method’s performance is similar to that of IG, and it is considerably better than PMI.

Item Type:Article (Published)
Uncontrolled Keywords:feature selection; text categorization; text categorisation
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Binary Information Press
Copyright Information:© 2007 Binary Information Press.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:16194
Deposited On:14 Jun 2011 14:47 by Shane Harper. Last Modified 10 Oct 2016 16:34

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