Classification for Imbalanced and Overlapping Classes Using Outlier Detection and Sampling Techniques

Abstract

In many real world applications, the example data among different pattern classes are imbalanced and overlapping, which hinder the classification performance of many learning algorithms. In this paper, data cleaning techniques based BNF (the borderline noise factor) is proposed to remove the borderline noise and three under-sampling methods are studied to select the representative majority class examples and remove the distant samples which are useless to form the decision boundary. BNF shows the degree of being a borderline noise and the outlier detection algorithm is improved to clean the whole dataset. Here G-mean (Geometric Mean) is used to define the threshold, which can improve the classification accuracy of minority classes while achieving better performance on the overall classification. The experimental results demonstrate the effectiveness of sampling method with data cleaning techniques based on BNF.