A Similarity-Based Classification Framework for Multiple-Instance Learning

Abstract

Multiple-instance learning (MIL) is a generalization of supervised learning that attempts to learn useful information from bags of instances. In MIL, the true labels of instances in positive bags are not available for training. This leads to a critical challenge, namely, handling the instances of which the labels are ambiguous (ambiguous instances). To deal with these ambiguous instances, we propose a novel MIL approach, called similarity-based multiple-instance learning (SMILE). Instead of eliminating a number of ambiguous instances in positive bags from training the classifier, as done in some previous MIL works, SMILE explicitly deals with the ambiguous instances by considering their similarity to the positive class and the negative class. Specifically, a subset of instances is selected from positive bags as the positive candidates and the remaining ambiguous instances are associated with two similarity weights, representing the similarity to the positive class and the negative class, respectively. The ambiguous instances, together with their similarity weights, are thereafter incorporated into the learning phase to build an extended SVM-based predictive classifier. A heuristic framework is employed to update the positive candidates and the similarity weights for refining the classification boundary. Experiments on real-world datasets show that SMILE demonstrates highly competitive classification accuracy and shows less sensitivity to labeling noise than the existing MIL methods.