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Grey-based multiple instance learning with multiple bag-representative

机译：基于灰色的多实例学习，具有多个袋代表

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摘要

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.
机译：多实例学习是监督学习的修改，处理收集实例的分类，该分类称为袋子。每个袋子包含许多实例，其中提取的功能。在多实例学习中，标准假设是阳性袋包含至少一个阳性实例，而负袋仅由负实例组成。多实例学习的复杂性大量依赖于训练数据集中的实例数。由于我们通常面临大型实例空间，因此设计高效的实例选择技术来加速培训过程，而不会影响性能。首先，本文提出了一种基于灰色关系分析的支持向量机的多实例学习模型。可以减少数据大小，并且可以预先判断袋子中的实例的重要性。其次，本文介绍了一种带有袋子代表选择器的算法，该算法基于袋级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的算法概括为多个类任务。实验研究评估并比较了我们对10个数据集的8现实多实例方法的方法的性能，然后演示了所提出的方法与最先进的多实例学习方法具有竞争力。

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