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

Publication ,  Journal Article
Malof, JM; Mazurowski, MA; Tourassi, GD
Published in: Neural Netw
January 2012

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.

Duke Scholars

Published In

Neural Netw

DOI

EISSN

1879-2782

Publication Date

January 2012

Volume

25

Issue

1

Start / End Page

141 / 145

Location

United States

Related Subject Headings

  • Mammography
  • Humans
  • Female
  • Decision Making, Computer-Assisted
  • Data Interpretation, Statistical
  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 4602 Artificial intelligence
 

Citation

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ICMJE
MLA
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Malof, J. M., Mazurowski, M. A., & Tourassi, G. D. (2012). The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support. Neural Netw, 25(1), 141–145. https://doi.org/10.1016/j.neunet.2011.07.002
Malof, Jordan M., Maciej A. Mazurowski, and Georgia D. Tourassi. “The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.Neural Netw 25, no. 1 (January 2012): 141–45. https://doi.org/10.1016/j.neunet.2011.07.002.
Malof, Jordan M., et al. “The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.Neural Netw, vol. 25, no. 1, Jan. 2012, pp. 141–45. Pubmed, doi:10.1016/j.neunet.2011.07.002.
Journal cover image

Published In

Neural Netw

DOI

EISSN

1879-2782

Publication Date

January 2012

Volume

25

Issue

1

Start / End Page

141 / 145

Location

United States

Related Subject Headings

  • Mammography
  • Humans
  • Female
  • Decision Making, Computer-Assisted
  • Data Interpretation, Statistical
  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 4602 Artificial intelligence