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Evolutionary Strategy to Perform Batch-Mode Active Learning on Multi-Label Data

机译：对多标签数据执行批处理模式主动学习的进化策略

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摘要

Multi-label learning has become an important area of research owing to the increasing number of real-world problems that contain multi-label data. Data labeling is an expensive process that requires expert handling. The annotation of multi-label data is laborious since a human expert needs to consider the presence/absence of each possible label. Consequently, numerous modern multi-label problems may involve a small number of labeled examples and plentiful unlabeled examples simultaneously. Active learning methods allow us to induce better classifiers by selecting the most useful unlabeled data, thus considerably reducing the labeling effort and the cost of training an accurate model. Batch-mode active learning methods focus on selecting a set of unlabeled examples in each iteration in such a way that the selected examples are informative and as diverse as possible. This article presents a strategy to perform batch-mode active learning on multi-label data. The batch-mode active learning is formulated as a multi-objective problem, and it is solved by means of an evolutionary algorithm. Extensive experiments were conducted in a large collection of datasets, and the experimental results confirmed the effectiveness of our proposal for better batch-mode multi-label active learning.
机译：由于越来越多的包含多标签数据的实际问题，多标签学习已成为研究的重要领域。数据标记是一个昂贵的过程，需要专家处理。多标签数据的注释很费力，因为人类专家需要考虑每个可能标签的存在/不存在。因此，许多现代的多标签问题可能同时涉及少量的标记示例和大量未标记的示例。主动学习方法使我们能够通过选择最有用的未标记数据来诱导更好的分类器，从而大大减少了标记工作量和训练准确模型的成本。批处理模式的主动学习方法着重于在每次迭代中选择一组未标记的示例，以使所选示例具有丰富的信息并尽可能地多样化。本文介绍了一种对多标签数据执行批处理模式主动学习的策略。批处理模式主动学习被公式化为一个多目标问题，并通过进化算法解决。在大量数据集中进行了广泛的实验，实验结果证实了我们的建议对于更好的批处理模式多标签主动学习的有效性。

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