C4.5 and Imbalanced Data sets: Investigating the eect of sampling method, probabilistic estimate, and decision tree structure

Imbalanced data sets are becoming ubiquitous, as many applications have very few instances of the \interesting" or \abnormal" class. Traditional machine learning algorithms can be biased towards majority class due to over-prevalence. It is desired that the interesting (minority) class prediction be improved, even if at the cost of additional majority class errors. In this paper, we study three issues, usually considered separately, concerning decision trees and imbalanced data sets | quality of probabilistic estimates, pruning, and eect of preprocessing the imbalanced data set by over or undersampling methods such that a fairly balanced training set is provided to the decision trees. We consider each issue independently and in conjunction with each other, highlighting the scenarios where one method might be preferred over another for learning decision trees from imbalanced data sets.