Multiple instance learning is a modification in supervised learning that handles the classification of collection instances, which called bags. Each bag contains a number of instances whose features are extracted. In multiple instance learning, the standard assumption is that a positive bag contains at least one positive instance, whereas a negative bag is only comprised of negative instances. The complexity of multiple instance learning relies heavily on the number of instances in the training datasets. Since we are usually confronted with a large instance space, it is important to design efficient instance selection techniques to speed up the training process, without compromising the performance. Firstly, a multiple instance learning model of support vector machine based on grey relational analysis is proposed in this paper. The data size can be reduced, and the importance of instances in the bag can be preliminarily judged. Secondly, this paper introduces an algorithm with the bag-representative selector that trains the support vector machine based on bag-level information. Finally, this paper shows how to generalize the algorithm for binary multiple instance learning to multiple class tasks. The experimental study evaluates and compares the performance of our method against 8 state-of-the-art multiple instance methods over 10 datasets, and then demonstrates that the proposed approach is competitive with the state-of-art multiple instance learning methods.

中文翻译：

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基于灰色的多实例学习，具有多个bag-代表

多实例学习是对有监督的学习的一种修改，它处理集合实例的分类（称为袋）。每个包包含许多提取其特征的实例。在多实例学习中，标准的假设是一个正面袋子至少包含一个正面实例，而一个负面袋子仅包含负面实例。多实例学习的复杂性在很大程度上取决于训练数据集中的实例数量。由于我们通常面临较大的实例空间，因此设计有效的实例选择技术以加快训练过程而又不影响性能非常重要。首先，提出了一种基于灰色关联分析的支持向量机的多实例学习模型。可以减小数据大小，并且可以初步判断包中实例的重要性。其次，本文介绍了一种具有袋代表选择器的算法，该算法根据袋级别信息训练支持向量机。最后，本文展示了如何将二进制多实例学习算法推广到多类任务。实验研究评估和比较了我们的方法与10个数据集上的8种最新的多实例方法的性能，然后证明了该方法与最新的多实例学习方法具有竞争力。本文介绍了一种具有袋代表选择器的算法，该算法根据袋级别信息训练支持向量机。最后，本文展示了如何将二进制多实例学习算法推广到多类任务。实验研究评估和比较了我们的方法与10个数据集上的8种最新的多实例方法的性能，然后证明了该方法与最新的多实例学习方法具有竞争力。本文介绍了一种具有袋代表选择器的算法，该算法根据袋级别信息训练支持向量机。最后，本文展示了如何将二进制多实例学习算法推广到多类任务。实验研究评估和比较了我们的方法与10个数据集上的8种最新的多实例方法的性能，然后证明了该方法与最新的多实例学习方法具有竞争力。