The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems

Jordan M. Malof, Maciej A. Mazurowski, Georgia D. Tourassi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper the effect of class imbalance in the case base of a case-based classifier is investigated as it pertains to case base reduction and the resulting classifier performance. A k-nearest neighbor algorithm is used as a classifier and the Random Mutation Hill Climbing (RMHC) algorithm is used for case base reduction. The effects at various levels of positive class prevalence are tested in a binary classification problem. The results indicate that class imbalance is detrimental to both case base reduction and classifier performance. Selection with RMHC generally improves the classification performance regardless of the case base prevalence.

Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages1975-1980
Number of pages6
DOIs
StatePublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/TerritoryUnited States
CityAtlanta, GA
Period06/14/0906/19/09

Keywords

  • Cased-based learning
  • Computer-aided decision
  • Imbalance

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