A Kolmogorov–Smirnov statistic based segmentation approach to learning from imbalanced datasets: With application in property refinance prediction

Classification is an important task in data mining. Class imbalance has been reported to hinder the performance of standard classification models. However, our study shows that class imbalance may not be the only cause to blame for poor performance. Rather, the underlying complexity of the problem may play a more fundamental role. In this paper, a decision tree method based on Kolmogorov–Smirnov statistic (K–S tree), is proposed to segment the training data so that a complex problem can be divided into several easier sub-problems where class imbalance becomes less challenging. K–S tree is also used to perform feature selection, which not only selects relevant variables but also removes redundant ones. After segmentation, a two-way re-sampling method is used at the segment level to empirically determine the optimal sampling percentage and the rebalanced data is used to fit logistic regression models, also at the segment level. The effectiveness of the proposed method is demonstrated through its application on property refinance prediction.

► Demonstrated the impact of class imbalance and data complexity on classification.
► Used a segmentation approach to reduce data complexity.
► Used KS statistic (insensitive to class distribution) for decision tree induction.
► Used two-way re-sampling to empirically determine optimal class distribution.
► Proposed method significantly outperform its peers in the application.