Feature subset selection bias for classification learning

Surendra K. Singhi, Huan Liu

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

36 Scopus citations


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 socalled 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 publicationACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006
Number of pages8
StatePublished - 2006
Event23rd International Conference on Machine Learning, ICML 2006 - Pittsburgh, PA, United States
Duration: Jun 25 2006Jun 29 2006

Publication series

NameACM International Conference Proceeding Series


Other23rd International Conference on Machine Learning, ICML 2006
Country/TerritoryUnited States
CityPittsburgh, PA

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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