Abstract
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.
Highlights
Standard pattern recognition problems consider that the objects are represented as a single feature vector which contains sufficient information for the recognition of these objects
Critical difference value has been added to the average rank of each multiple instance learning (MIL) approach in order to analyze whether the proposed approach is significantly better than the other MIL approaches
This study has focused on multiple instance learning in which a classifier learns from a set of feature vectors instead of single feature vector and has proposed an MIL approach based on Twin Support Vector Machine (TWSVM), termed as MIL-TWSVM
Summary
Standard pattern recognition problems consider that the objects are represented as a single feature vector which contains sufficient information for the recognition of these objects. Traditional supervised learning techniques handle such kind of problems by representing complex objects using single feature vector. This reduction may lose significant information which further degrades the performance of supervised learning techniques. In MIL framework, an object is represented by a set of feature vectors or a bag and the classifier trains at bag level instead of instance level and predicts the class label of a bag instead of an instance. This study has focused on the first category and extended the recently proposed Twin Support Vector Machine (TWSVM) classifier to multiple instance learning scenarios by obtaining summarized information of each bag using different dissimilarity measures [27]. This paper proposes a bag dissimilarity based multiple instance learning TWSVM (MIL-TWSVM) classifier.
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