A genetic algorithm-based approach for class-imbalanced learning
Yongcheng Wu, Shangyan Dong · 2018
It is often the case for machine learning that datasets are imbalanced in the real world. When dealing with this problem, the traditional classification method aiming to maximize the overall accuracy of classification is not suitable. To tackle this issue and improve the performance of classifiers, methods based on oversampling, undersampling and cost-sensitive classification are widely employed. In this paper, we propose a new genetic algorithm-based over-sampling technique for class-imbalanced datasets. The genetic algorithm can create optimized synthetic minority class instances to produce a balanced training datasets. The experimental results on 5 class-imbalanced datasets show that our method performs better than three existing sampling techniques in terms of AUC and F-measure.