Multiple instance learning: A survey of problem characteristics and applications
Multiple instance learning: A survey of problem characteristics and applications
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DOI：
10.1016/j.patcog.2017.10.009
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发表时间：
2018-05-01
影响因子：
8
通讯作者：
Gagnon, Ghyslain
中科院分区：
文献类型：
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作者：
Carbonneau, Marc-Andre;Cheplygina, Veronika;Gagnon, Ghyslain
多实例学习（MIL）是弱监督学习的一种形式，其中训练实例被安排在集合中，称为袋子，并为整个袋子提供标签。这个公式越来越受到关注，因为它自然适合各种问题，并允许利用弱标记数据。因此，它已被用于各种应用领域，如计算机视觉和文档分类。然而，从包中学习提出了重要的挑战，是独特的MIL。本文提供了一个全面的调查的特点，定义和区分类型的MIL问题。到目前为止，这些问题特征尚未得到正式识别和描述。因此，MIL算法的性能从一个数据集到另一个的变化是很难解释的。在本文中，MIL问题的特点分为四大类：组成的袋子，数据分布的类型，模糊的实例标签，和要执行的任务。专门处理每个类别的方法进行了审查。然后，在何种程度上，这些特点体现在关键的MIL应用领域进行了描述。最后，进行实验，以比较16个国家的最先进的MIL方法的性能选择的问题特征。本文提供了洞察力的问题特性如何影响MIL算法，建议为未来的基准和有前途的研究途径。代码可在https://github.com/macarbonneau/MILSurvey在线获得。(C)2017爱思唯尔有限公司版权所有
Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research. Code is available on-line at https://github.com/macarbonneau/MILSurvey. (C) 2017 Elsevier Ltd. All rights reserved.