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Boosting decision stumps for dynamic feature selection on data streams

机译：增强决策树桩，以选择数据流上的动态特征

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Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease the impact of the curse of dimensionality and enhancing the generalization rates of classifiers. In data streams, classifiers shall benefit from all the items above, but more importantly, from the fact that the relevant subset of features may drift over time. In this paper, we propose a novel dynamic feature selection method for data streams called Adaptive Boosting for Feature Selection (ABFS). ABFS chains decision stumps and drift detectors, and as a result, identifies which features are relevant to the learning task as the stream progresses with reasonable success. In addition to our proposed algorithm, we bring feature selection-specific metrics from batch learning to streaming scenarios. Next, we evaluate ABFS according to these metrics in both synthetic and real-world scenarios. As a result, ABFS improves the classification rates of different types of learners and eventually enhances computational resources usage. (C) 2019 Elsevier Ltd. All rights reserved.
机译：特征选择的目标是确定数据集中哪些特征与学习任务有关。它也是众所周知的，并用于改善计算时间，减少计算要求并减少维数诅咒的影响并提高分类器的泛化率。在数据流中，分类器应受益于上述所有项目，但更重要的是受益于特征的相关子集可能随时间漂移的事实。在本文中，我们为数据流提出了一种新颖的动态特征选择方法，称为“特征选择的自适应增强”。 ABFS链接决策树桩和漂移检测器，结果，随着流以合理的成功进行，识别出哪些特征与学习任务相关。除了我们提出的算法外，我们还将特定于功能选择的指标从批处理学习流式传输到场景中。接下来，我们在合成和实际场景中根据这些指标评估ABFS。结果，ABFS提高了不同类型学习者的分类率，并最终提高了计算资源的利用率。 （C）2019 Elsevier Ltd.保留所有权利。

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