A novel cost sensitive neural network ensemble for multiclass imbalance data learning

Traditional classification algorithms can be limited in their performance on imbalanced datasets. In recent years, the imbalanced data learning problem has drawn significant interest. In this work, we focus on designing modifications to neural network, in order to appropriately tackle the problem of multiclass imbalance. We propose a method that combines two ideas: diverse random subspace ensemble learning with evolutionary search, to improve the performance of neural network on multiclass imbalanced data. An evolutionary search technique is utilized to optimize the misclassification cost under the guidance of imbalanced data measures. Moreover, the diverse random subspace ensemble employs the minimum overlapping mechanism to provide diversity so as to improve the performance of the learning and optimization of neural network. Furthermore, the ensemble framework can determine the optimal amount of non-redundant components automatically. We have demonstrated experimentally using UCI datasets that our approach can achieve significantly better result than state-of-the-art methods for imbalanced data.