Minority Oversampling Using Sensitivity

The Synthetic Minority Oversampling Technique (SMOTE) is effective to handle imbalance classification problems. However, the random candidate selection of SMOTE may lead to severe overlap between classes and introduce new noise factors. Many variants of SMOTE have been proposed to relieve these problems by generating new examples in safe regions. Most of these methods generate new examples with existing minority examples without considering the negative impact that class imbalance have brought on these examples. In this paper, we handle the imbalance classification using Bayes' decision rule and propose a novel oversampling method, the Minority Oversampling using Sensitivity (MOSS). Candidates for new example generations are selected considering their sensitivity with respect to class imbalance. New examples are then generated by interpolating the candidate and one of its adjacent examples. Experiments on 30 datasets confirm the superiority of the MOSS against one baseline method and seven oversampling methods.