A Novel Minority Cloning Technique for Cost-Sensitive Learning

Abstract

In many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, the class-imbalanced cost-sensitive learning has attracted much attention from researchers. Sampling is one of the widely used techniques in dealing with the class-imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we propose a novel Minority Cloning Technique (MCT) for class-imbalanced cost-sensitive learning. MCT alters the class distribution of training data by cloning each minority class instance according to the similarity between it and the mode of the minority class. The experimental results on a large number of UCI datasets show that MCT performs much better than Minority Oversampling with Replacement Technique (MORT) and Synthetic Minority Oversampling TEchnique (SMOTE) in terms of the total misclassification costs of the built classifiers.