Multiple instance classification: Review, taxonomy and comparative study
Multiple instance classification: Review, taxonomy and comparative study
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DOI：
10.1016/j.artint.2013.06.003
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发表时间：
2013-08-01
影响因子：
14.4
通讯作者：
Amores, Jaume
中科院分区：
文献类型：
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作者：
Amores, Jaume
多个实例学习（MIL）已成为模式识别界的重要话题，到目前为止，已经提出了许多解决此问题的解决方案。尽管这一事实，缺乏比较研究，可以阐明不同方法的特征和行为。在这项工作中，我们提供了针对分类任务的分析（即，遗漏了其他学习任务，例如回归）。为了进行我们的研究，我们实施了14种分为三个不同家庭的方法。我们分析了各种知名数据库的方法的性能，并且还研究了它们在合成场景中的行为，以突出它们的特征。由于这项分析，我们得出结论，提取全球行李级信息的方法总体上表明表现显然是出色的。从这个意义上讲，分析使我们能够理解为什么某些类型的方法比其他方法更成功，这使我们能够在设计新的MIL方法的设计中建立准则。 （c）2013 Elsevier B.V.保留所有权利。
Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problem have been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e., leaving out other learning tasks such as regression). In order to perform our study, we implemented fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods. (c) 2013 Elsevier B.V. All rights reserved.