Multiple Instance Learning for bags with Ordered instances
Multiple Instance Learning (MIL) algorithms are designed for problems where labels are available for groups of instances, commonly referred to as bags. In this paper, we consider a new MIL prob- lem setting where instances in a bag are not ex- changeable, and a bijection exists between every pair of bags. We propose a neural network based MIL algorithm (MILOrd) that leverages the exis- tence of such a bijection when learning to discrim- inate bags. MILOrd has an input node for each in- stance in the bag, an output node that captures the bag level prediction, and a hidden layer that cap- tures the output from an instance level classifier for each instance in the bag. The bag level prediction is obtained by combining these hidden layer val- ues using a function that models the importance of each instance, unlike the traditional schemes where each instance is considered equal. We demonstrate the utility of the proposed algorithm on the prob- lem of burned area mapping using yearly bags com- posed of multispectral reflectance data for different time steps in the year. Our experiments show that MILOrd outperforms traditional MIL schemes that don’t account for the presence of a bijection.