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

Divya Tomar, Sonali Agarwal
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引用次数: 3

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.
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基于袋不相似度的双支持向量机多实例学习
在多实例学习(MIL)框架中,对象由一组称为bag的实例表示。如果包包含至少一个阳性实例,则为其分配阳性类标签;否则,一个袋子被贴上负类标签。因此,MIL的任务是在包级别而不是实例级别学习分类器。传统的监督学习方法不能直接应用于这种情况。在这项研究中,我们用训练数据集中每个袋子与其他现有袋子的不同之处的向量来表示每个袋子,并提出了一个基于多实例学习的双支持向量机(MIL-TWSVM)分类器。我们用不同的方式来表示两个袋子之间的差异,并对它们进行了比较分析。在10个基准MIL数据集上的实验结果表明,所提出的MIL- twsvm分类器计算成本低,与最先进的方法相比具有竞争力。采用Friedman统计和Nemenyi事后检验对实验结果的显著性进行了检验。
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