Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning
提出一种结合聚类合成数据生成与进化算法的集成框架，在40个不平衡数据集上优于现有方法，帮助处理分类中样本数量不均的问题。
Class imbalance problems, where the number of samples in each class is unequal, is prevalent in numerous real world machine learning applications. Traditional methods which are biased toward the majority class are ineffective due to the relative severity of misclassifying rare events. This paper proposes a novel evolutionary cluster-based oversampling ensemble framework, which combines a novel cluster-based synthetic data generation method with an evolutionary algorithm (EA) to create an ensemble. The proposed synthetic data generation method is based on contemporary ideas of identifying oversampling regions using clusters. The novel use of EA serves a twofold purpose of optimizing the parameters of the data generation method while generating diverse examples leveraging on the characteristics of EAs, reducing overall computational cost. The proposed method is evaluated on a set of 40 imbalance datasets obtained from the University of California, Irvine, database, and outperforms current state-of-the-art ensemble algorithms tackling class imbalance problems.