MiDivCon: Framework and method for Multiple Instance Learning

Abstract

We present a new framework and method for solving Multiple Instance Learning (MIL) problems. As a variation on supervised learning, MIL addresses the problem of classifying a bag of instances. If at least one of the instances in a bag is positive the bag is labeled positive, otherwise it is negative. We use a divide and conquer strategy to identify true positive group of instances in the positive bags and use Bayesian statistics to minimize the false positive instances in the same bags. After testing on benchmark data we also use the method on a challenging task of predicting behavior from molecular profiles data. Comparison results show that our method performs on par or better than other MIL methods.