Online streaming feature selection with incremental feature grouping,WIREs Data Mining and Knowledge Discovery

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Online streaming feature selection with incremental feature grouping
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2020-03-17 , DOI: 10.1002/widm.1364
Noura Al Nuaimi ₁ , Mohammad M. Masud ₁

Affiliation

Today, the dimensionality of data is increasing in a massive way. Thus, traditional feature selection techniques are not directly applicable. Consequently, recent research has led to the development of a more efficient approach to the selection of features from a feature stream, known as streaming feature selection. Another active research area, related to feature selection, is feature grouping. Feature grouping selects relevant features by evaluating the hidden information of selected features. However, although feature grouping is a promising technique, it is not directly applicable to feature streams. In this paper, we propose a novel and efficient algorithm that uses online feature grouping, embedded within a new incremental technique, to select features from a feature stream. This technique groups similar features together; it assigns new incoming features to an existing group or creates a new group. To the best of our knowledge, this is the first approach that proposes the use of incremental feature grouping to perform feature selection from features. We have implemented this algorithm and evaluated it, using benchmark datasets, against state‐of‐the‐art streaming feature selection algorithms that use feature grouping or incremental selection techniques. The results show superior performance by the proposed technique through combining the online selection and grouping, in terms of prediction accuracy and running time.

中文翻译：

具有增量功能分组的在线流功能选择

如今，数据的规模正在以巨大的方式增长。因此，传统特征选择技术不能直接应用。因此，最近的研究导致开发了一种从特征流中选择特征的更有效方法，称为流特征选择。与特征选择有关的另一个活跃的研究领域是特征分组。特征分组通过评估选定特征的隐藏信息来选择相关特征。但是，尽管特征分组是一种很有前途的技术，但它并不直接适用于特征流。在本文中，我们提出了一种新颖有效的算法，该算法使用嵌入新的增量技术中的在线特征分组从特征流中选择特征。这种技术将相似的特征组合在一起。它将新的传入功能分配给现有组或创建一个新组。据我们所知，这是第一种建议使用增量要素分组从要素中进行要素选择的方法。我们已经实现了该算法，并使用基准数据集针对使用特征分组或增量选择技术的最新流特征选择算法进行了评估。结果表明，通过将在线选择和分组相结合，所提技术在预测准确度和运行时间方面均具有优异的性能。我们已经实施了该算法，并使用基准数据集针对使用特征分组或增量选择技术的最新流特征选择算法进行了评估。结果表明，通过将在线选择和分组相结合，所提技术在预测准确度和运行时间方面均具有优异的性能。我们已经实施了该算法，并使用基准数据集针对使用特征分组或增量选择技术的最新流特征选择算法进行了评估。结果表明，通过将在线选择和分组相结合，所提技术在预测准确度和运行时间方面均具有优异的性能。

更新日期：2020-03-17

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