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

Authors:
Jordan M. Malof;Maciej A. Mazurowski;Georgia D. Tourassi
Affiliations:
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY;Department of Radiology, Duke University Medical Center, Durham, NC;Department of Radiology, Duke University Medical Center, Durham, NC
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 5
Cited 1

Instance-Based Learning Algorithms

Machine Learning
Introduction to artificial neural systems

Introduction to artificial neural systems
Reduction Techniques for Instance-BasedLearning Algorithms

Machine Learning
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition

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

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.