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Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems

机译:模拟问题的多实例主动学习的袋级聚合

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摘要

A growing number of applications, e.g., video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data, while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data are arranged into sets, called bags, which are weakly labeled. Most AL methods focus on single-instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance AL (MIAL). The aggregated informativeness method identifies the most informative instances based on classifier uncertainty and queries bags incorporating the most information. The other proposed method, called cluster-based aggregative sampling, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single-instance AL methods.
机译:越来越多的应用程序,例如视频监控和医学图像分析,需要培训识别系统从大量弱弱注释数据,而允许与域专家的一些有针对性的互动改善培训过程。在这种情况下,主动学习(AL)可以通过查询专家提供大多数信息实例的标签来减少培训分类器的标签成本。本文重点介绍了多实例学习(MIL)中的实例分类问题的方法,其中数据被安排成集合,称为袋子,这是弱标记的。大多数Al方法都侧重于单程学习问题。这些方法不适合MIL问题,因为它们无法解释数据的袋结构。本文提出了多实例al(MIAL)提出了对实例信息性的袋级聚合的新方法。聚合的信息性方法根据分类器的不确定性和查询袋来识别最多的信息,并包含最多的信息。其他提出的方法,称为基于群集的聚合采样,在实例空间中分层地群集数据。通过考虑袋标签,推断的实例标签以及仍在集群中的标签的比例来评估实例的信息性。两个所提出的方法在广泛的实验中使用来自多个应用领域的基准数据显着优于参考方法。结果表明,使用适当的策略解决MIL问题,在实现与单一实例AL方法中实现相同的性能所需的查询数量会产生显着减少。

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