A bag oversampling approach for class imbalance in multiple instance learning

Abstract

Multiple Instance Learning (MIL) is a relatively new learning paradigm which allows to train a classifier with weakly labelled data. In spite that the community has been developing different methods to learn from this kind of data, there is little discussion on how to proceed when there is an imbalanced representation of the classes. The class imbalance problem in MIL is more complex compared with their counterpart in single-instance learning because it may occur at instance and/or bag level, or at both. Here, we propose an oversampling approach at bag level in order to improve the representation of the minority class. Experiments in nine benchmark data sets are conducted to evaluate the proposed approach.