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In multiple instance learning (MIL) framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM) classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities