Multi-class imbalance problem: A multi-objective solution

Abstract

Multi-class imbalance problems are frequently encountered in real-world applications of machine learning. They have fundamentally complex trade-offs between classes. Existing literature tends to use a predetermined rebalancing strategy and mainly focuses on overall performance measures. However, in many real-world problems, the true level of imbalance and the relative importance between classes are unknown, making it difficult to predetermine the rebalancing strategy and the evaluation criterion. In this paper, we explicitly consider the between-class trade-off issue in the multi-class imbalance problem. We consider all the classes to be important and find a set of optimal trade-offs for the decision-maker to choose from. To reduce the computational cost of this process and make it a practical method, we seek the help of selective ensemble and multiple undersampling rates, and propose the Multi-class Multi-objective Selective Ensemble (MMSE) framework. We further equip the objective modeling with margins to reduce the number of objectives when the task has many classes. Experimental results show that our proposed methods successfully obtain diverse and highly competitive solutions within an acceptable running time.