Manifold cluster-based evolutionary ensemble imbalance learning

Abstract

For an imbalanced dataset, traditional machine learning methods usually misclassify minority samples due to the indicator evaluating classification accuracy biased toward majority class. To address the issue, manifold cluster-based evolutionary ensemble imbalance learning is proposed, with the purpose of providing a more effective framework for building an optimal imbalance classifier. After mapping the original data to manifold space, majority samples are removed from each sub-cluster in terms of their distribution characteristic. Following that, a new one is generated in each minority sub-cluster by over-sampling, with the purpose of avoiding a misclassified new minority sample that produced from small disjuncts. In above manifold clustering-based resampling techniques, optional operations and key parameters for normalization, manifold learning, clustering, under-sampling and over-sampling form various combination. Thus, evolutionary algorithm is introduced to seek the optimal structure for MECS-Ensemble. Each individual is encoded by five integer and six real number, and a fitness function is designed to evaluate its classification accuracy and the diversity of majority samples. The statistical experimental results for 39 imbalanced datasets show that MECS-Ensemble proposed in the paper is superior to the other imbalance learning methods, especially, manifold clustering-based resampling technique contributes to significant performance improvements.