Revisiting milis multiple instance learning algorithm with a different instance selection mechanism

Abstract

Multiple instance learning (MIL) is a new paradigm in machine learning that deals with classification of bags of instances, as opposed to the traditional view that aims at learning from single instances. In a typical MIL setting, a negative bag is composed of only negative instances. On the other hand, a bag is considered positive if it contains at least one positive instance. This learning approach provides a natural way of modeling several pattern recognition and computer vision problems, e.g. protein, document and image classification and object recognition, which inherently require learning under ambiguity. In many cases, MIL approaches performs better than the standard single instance learning (SIL) methods. One of the general approaches to MIL is to transform a given MIL problem into a corresponding SIL problem. This transformation is mainly done with selecting a set of representative instances from the training bags, and these group of studies basically differ from each other on how they perform this instance selection step. In this study, we revisit such a MIL approach called MILIS with a different instance selection mechanism. The experimental results show that the proposed approach performs better on MIL benchmark data sets as compared to the original algorithm.