Measure optimized wrapper framework for multi-class imbalanced data learning: An empirical study

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Many methods have been proposed to address and attempt to solve the problem, including re-sampling and cost-sensitive learning. However, the existing methods have room for improvement since the potentially optimal values of the factors associated with best performance are unknown. Moreover most methods only focus on the binary class imbalance problem, thus there is no efficient solution in multi-class imbalanced learning. This paper presents an effective wrapper framework incorporating the evaluation measure into the objective function of cost sensitive learning as well as re-sampling directly, so as to improve the original methods through optimizing factors influencing the performance on the imbalanced data classification. Comprehensive experimental results on various standard benchmark datasets with different ratios of imbalance show that the influence of optimizing parameters on the solutions for learning imbalanced data is critical, and demonstrate the effectiveness of measure-optimized scheme on the imbalanced data learning.