EVOLUTIONARY-BASED ENSEMBLE UNDER-SAMPLING FOR IMBALANCED DATA

Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. The classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. Therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. The advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods.