Resampling and Cost-Sensitive Methods for Imbalanced Multi-instance Learning

Multi-instance learning uses a set of bags containing many instances, which makes it different from standard propositional classification. Our research shows that, similar to the single-instance imbalance problem, classification of multi-instance data with imbalanced class distributions significantly degrades performance when compared to most standard multi… CONTINUE READING