A fuzzy citation-kNN algorithm for multiple instance learning

In multiple instance learning (MIL) setting, instances are grouped together in different labeled bags and the classifier tries to learn the label of unknown bags or instances. This is significantly different from traditional supervised learning techniques where the instances are labeled itself. In this work, a fuzzy based citation-kNN technique, which uses modified Hausdorff distance between bags, is introduced. Introduction of a fuzzy distance measure helps to solve the problem of overlapping bags. Effect of false positive instances in a positive bag are also reduced by calculating a fuzzy class membership for the training bags. Experiments on drug discovery and image datasets show that the performance of the proposed algorithm (MI-FCKNN) is better than the traditional citation-kNN and competitive with most state-of-the-art algorithms.