Abstract: Class imbalance problem is an important challenge in supervised machine learning field.In an imbalanced training set,although the minority class is significantly outnumbered by the majority class,it usually attracts more attention from the practitioners and has higher misclassification cost than the latter one.Most classifier learning algorithms usually employ the overall classification accuracy as the optimization goal,and thus easily misclassify the minority class examples that make less contribution to overall classification accuracy.Existing imbalance learning approaches often utilize the class imbalance ratio(IR) of a training set as the classification complexity measure as well as the optimization goal.However,it has recently been indicated that,compared with IR,class overlap can more objectively measure the learning difficulty of an imbalanced dataset.Considering the importance of class overlap in evaluating the data complexity,the imbalance problem is solved from the class overlap perspective,and an imbalanced dataset learning approach FO-RBU utilizing the class overlap information of a training set is proposed.Specifically,the distribution concerning the ratios of feature based class overlap examples is employed to evaluate the learning difficulty of an imbalanced dataset,and further utilized as a theoretical guideline in determining the proper undersampling extent of Radial-Based Undersampling approach.Experimental results show that the feature values based class overlap information is a good indicator in the proper undersampling ratio determination process,and the proposed class imbalance learning approach FO-RBU is effective.

Key words: Classification, Class imbalanced data, Class imbalance ratio IR, Class overlap, Undersampling, Radial-based undersampling, Undersampling ratio, Machine learning