Hierarchical sampling for multi-instance ensemble learning

In this paper, we propose a Hierarchical Sampling-based Multi-Instance ensemble LEarning (HSMILE) method. Due to the unique multi-instance learning nature, a positive bag contains at least one positive instance whereas samples (instance and sample are interchangeable terms in this paper) in a negative bag are all negative, simply applying bootstrap sampling to individual bags may severely damage a positive bag because a sampled positive bag may not contain any positive sample at all. To solve the problem, we propose to calculate probable positive sample distributions in each positive bag and use the distributions to preserve at least one positive instance in a sampled bag. The hierarchical sampling involves inter- and intrabag sampling to adequately perturb bootstrap sample sets for multi-instance ensemble learning. Theoretical analysis and experiments confirm that HSMILE outperforms existing multi-instance ensemble learning methods.