Feature subset selection bias for classification learning

Surendra K. Singhi, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

Feature selection is often applied to high-dimensional data prior to classification learning. Using the same training dataset in both selection and learning can result in so-called feature subset selection bias. This bias putatively can exacerbate data over-fitting and negatively affect classification performance. However, in current practice separate datasets are seldom employed for selection and learning, because dividing the training data into two datasets for feature selection and classifier learning respectively reduces the amount of data that can be used in either task. This work attempts to address this dilemma. We formalize selection bias for classification learning, analyze its statistical properties, and study factors that affect selection bias, as well as how the bias impacts classification learning via various experiments. This research endeavors to provide illustration and explanation why the bias may not cause negative impact in classification as much as expected in regression.

Original languageEnglish (US)
Title of host publicationICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
Pages849-856
Number of pages8
StatePublished - Oct 6 2006
EventICML 2006: 23rd International Conference on Machine Learning - Pittsburgh, PA, United States
Duration: Jun 25 2006Jun 29 2006

Publication series

NameICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
Volume2006

Other

OtherICML 2006: 23rd International Conference on Machine Learning
CountryUnited States
CityPittsburgh, PA
Period6/25/066/29/06

ASJC Scopus subject areas

  • Engineering(all)

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