Designing bag-level multiple-instance feature-weighting algorithms based on the large margin principle

In multiple-instance learning (MIL), class labels are attached to bags instead of instances, and the goal is to predict the class labels of unseen bags. Existing MIL algorithms generally fall into two types: those designed at the bag level and those designed at the instance level. In this paper, we aim to employ bags directly as learning objects and design multiple-instance feature-weighting algorithms at the bag level. In particular, we initially provide a brief introduction of the bag-level large margin feature-weighting framework and then adopt the three bag-level distances minimal Hausdorff (minH), class-to-bag (C2B) and bag-to-bag (B2B) as examples to design the corresponding bag-level feature-weighting algorithms. Experiments conducted on synthetic and real-world datasets empirically demonstrate the effectiveness of our work in improving MIL performances.