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Combining example selection with instance selection to speed up multiple-instance learning

机译：结合实例选择和实例选择以加快多实例学习

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摘要

Recently, several instance selection-based methods have been presented to solve the multiple-instance learning (MIL) problem. The basic idea is converting MIL into standard supervised learning by selecting some representative instance prototypes from the training set. However, training examples are not single instances but bags composed of one or more instances in MIL, so the computational complexity is often very high. Previous methods consider this issue only from the perspective of instance selection not from that of example selection. In this paper, we try to address this issue via combining example selection with instance selection. Three general example selection methods are derived by adapting three immune-inspired algorithms to MIL Additionally, we propose a simple instance selection method for MIL based on the probability that an instance is positive given a set of negative instances. Our example selection methods are combined with the new MIL method and other previous instance selection-based ones as a preprocessing step. The theoretical analysis and empirical results show that our MIL method is competitive to the state-of-the-art and the proposed example selection methods could significantly speed up various instance selection-based MIL methods with slightly weakening their performance or even strengthening it.
机译：最近，已经提出了几种基于实例选择的方法来解决多实例学习（MIL）问题。基本思想是通过从训练集中选择一些具有代表性的实例原型，将MIL转换为标准的监督学习。但是，训练示例不是单个实例，而是MIL中一个或多个实例组成的包，因此计算复杂度通常很高。以前的方法仅从实例选择的角度考虑此问题，而不是从实例选择的角度考虑。在本文中，我们尝试通过将示例选择与实例选择相结合来解决此问题。通过将三种免疫启发式算法应用于MIL，可以得出三种通用的示例选择方法。此外，我们针对给定的一组否定实例，一个实例为阳性的可能性，为MIL提出了一种简单的实例选择方法。我们的示例选择方法与新的MIL方法以及其他基于先前实例选择的方法相结合，作为预处理步骤。理论分析和经验结果表明，我们的MIL方法与最新技术相比具有竞争力，所提出的示例选择方法可以显着加快各种基于实例选择的MIL方法，而它们的性能甚至会有所下降甚至增强。

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