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

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.