The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.

Published

Journal Article

Case selection is a useful approach for increasing the efficiency and performance of case-based classifiers. Multiple techniques have been designed to perform case selection. This paper empirically investigates how class imbalance in the available set of training cases can impact the performance of the resulting classifier as well as properties of the selected set. In this study, the experiments are performed using a dataset for the problem of detecting breast masses in screening mammograms. The classification problem was binary and we used a k-nearest neighbor classifier. The classifier's performance was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) measure. The experimental results indicate that although class imbalance reduces the performance of the derived classifier and the effectiveness of selection at improving overall classifier performance, case selection can still be beneficial, regardless of the level of class imbalance.

Full Text

Duke Authors

Cited Authors

  • Malof, JM; Mazurowski, MA; Tourassi, GD

Published Date

  • January 2012

Published In

Volume / Issue

  • 25 / 1

Start / End Page

  • 141 - 145

PubMed ID

  • 21820273

Pubmed Central ID

  • 21820273

Electronic International Standard Serial Number (EISSN)

  • 1879-2782

Digital Object Identifier (DOI)

  • 10.1016/j.neunet.2011.07.002

Language

  • eng

Conference Location

  • United States