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首页> 外文期刊>International journal of machine learning and cybernetics >Multiple-instance learning via multiple-point concept based instance selection
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Multiple-instance learning via multiple-point concept based instance selection

机译：通过基于多点概念的实例选择的多实例学习

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摘要

Multiple-instance learning (MIL) is a kind of weakly supervised learning where a single label is assigned to a bag of instances. To solve MIL problems, researchers have presented an effective embedding based framework that projects bags into a new feature space, which is constructed from some selected instances that can represent target concepts to some extent. Most previous studies use single-point concepts for the instance selection, where every possible concept is represented by only a single point (i.e., instance). However, multiple points may be more powerful for the same concept than a single. In this paper, we propose the notion of multiple-point concept, jointly represented by a group of similar points, and then build an iterative instance-selection method for MIL upon Multiple-Point Concepts. The proposed algorithm is thus named MILMPC, and its main difference from other MIL algorithms is selecting instances via multiple-point concept rather than single-point concept. The experimental results on five data sets have validated the convergence of the iterative instance-selection method, and the generality of the resulting MIL model in that it performs consistently well under three different kinds of relevance evaluation criteria (used to measure the relevance of a candidate concept to the target). Furthermore, compared to other MIL algorithms, the proposed model has been demonstrated not only suitable for common MIL problems, but more suitable for hybrid problems.
机译：多实例学习（MIL）是一种弱监督的学习，其中单个标签被分配给一袋实例。为了解决问题MIL，研究人员已经提出了一种有效的基于嵌入框架，项目袋装入一个新的特征空间，这是从能代表目标的概念在一定程度上一些选定的实例构造。最先前的研究使用用于实例选择的单点概念，其中每个可能的概念仅由单点（即，实例）表示。但是，对于相同的概念，多个点可能比单个相同的概念更强大。在本文中，我们提出了多点概念的概念，由一组类似点共同表示，然后在多点概念时构建MIL的迭代实例选择方法。因此，所提出的算法名为MILMPC，与其他MIL算法的主要区别在于通过多点概念而不是单点概念选择实例。五个数据集的实验结果已经验证了迭代实例选择方法的收敛，以及所产生的MIL模型的一般性，因为它在三种不同类型的相关评估标准下始终如一地执行（用于衡量候选者的相关性概念到目标）。此外，与其他MIL算法相比，所提出的模型不仅适用于共同的MIL问题，而且还适合混合问题。

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