A cluster-based hybrid sampling approach for imbalanced data classification.

When processing instrumental data by using classification approaches, the imbalanced dataset problem is usually challenging. As the minority class instances could be overwhelmed by the majority class instances, training a typical classifier with such a dataset directly might get poor results in classifying the minority class. We propose a cluster-based hybrid sampling approach CUSS (Cluster-based Under-sampling and SMOTE) for imbalanced dataset classification, which belongs to the type of data-level methods and is different from previously proposed hybrid methods. A new cluster-based under-sampling method is designed for CUSS, and a new strategy to set the expected instance number according to data distribution in the original training dataset is also proposed in this paper. The proposed method is compared with five other popular resampling methods on 15 datasets with different instance numbers and different imbalance ratios. The experimental results show that the CUSS method has good performance and outperforms other state-of-the-art methods.