Jump to content United States-English
HP.com Home Products and Services Support and Drivers Solutions How to Buy
» Contact HP

HP.com home

Technical Reports


HP Labs

» Research
» News and events
» Technical reports
» About HP Labs
» Careers @ HP Labs
» Worldwide sites
» Downloads
Content starts here

Click here for full text: PDF

A Pitfall and Solution in Multi-Class Feature Selection for Text Classification

Forman, George


Keyword(s): benchmark comparison; text classification; information retrieval; F-measure; precision in the top 10; small training sets; skewed/unbalanced class distribution

Abstract: Information Gain is a well-known and empirically proven method for high-dimensional feature selection. We found that it and other existing methods failed to produce good results on an industrial text classification problem. On investigating the root cause, we find that a large class of feature scoring methods suffers a pitfall: they can be blinded by a surplus of strongly predictive features for some classes, while largely ignoring features needed to discriminate difficult classes. In this paper we demonstrate this pitfall hurts performance even for a relatively uniform text classification task. Based on this understanding, we present solutions inspired by round-robin scheduling that avoid this pitfall, without resorting to costly wrapper methods. Empirical evaluation on 19 datasets shows substantial improvements. Notes: Published in and presented at the 21st International Conference on Machine Learning, 4-8 July 2004, Banff, Alberta, Canada

8 Pages

Back to Index

»Technical Reports

» 2009
» 2008
» 2007
» 2006
» 2005
» 2004
» 2003
» 2002
» 2001
» 2000
» 1990 - 1999

Heritage Technical Reports

» Compaq & DEC Technical Reports
» Tandem Technical Reports
Printable version
Privacy statement Using this site means you accept its terms Feedback to HP Labs
© 2009 Hewlett-Packard Development Company, L.P.