A Novel Hybrid-Based Ensemble for Class Imbalance Problem

作者：Guo, Huaping^*; Zhou, Jun; Wu, Chang-an; She, Wei

来源：International Journal on Artificial Intelligence Tools, 2018, 27(6): 1850025.

DOI：10.1142/S0218213018500252

• Imbalanced problem
• ensemble
• under-sampling
• feature projection

Class-imbalance is very common in real world. However, conventional advanced methods do not work well on imbalanced data due to imbalanced class distribution. This paper proposes a simple but effective Hybrid-based Ensemble (HE) to deal with two-class imbalanced problem. HE learns a hybrid ensemble using the following two stages: (1) learning several projection matrixes from the rebalanced data obtained by under-sampling the original training set and constructing new training sets by projecting the original training set to different spaces defined by the matrixes, and (2) under-sampling several subsets from each new training set and training a model on each subset. Here, feature projection aims to improve the diversity between ensemble members and under-sampling technique is to improve generalization ability of individual members on minority class. Experimental results show that, compared with other state-of-the-art methods, HE shows significantly better performance on measures of AUC, G-mean, F-measure and recall.

• 出版日期2018-9
• 单位郑州大学; 信阳师范学院