In this paper, a novel inverse random under sampling (IRUS) method is proposed for class imbalance problem. The main idea is to severely under sample the negative class (majority class), thus creating a large number of distinct negative training sets. For each training set we then find a linear discriminant which separates the positive class from the negative class. By combining the multiple designs through voting, we construct a composite between the positive class and the negative class. The proposed methodology is applied on 11 UCI data sets and experimental results indicate a significant increase in Area Under Curve (AUC) when compared with many existing class-imbalance learning methods.
['Muhammad Atif Tahir', 'Josef Kittler', 'Krystian Mikolajczyk', 'Fei Yan']
A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling