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Online streaming feature selection with incremental feature grouping
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DOI：10.1002/widm.1364.png)
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Abstract
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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. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Data Preprocessing Technologies > Classification Technologies > Machine Learning
Keywords:
feature selection
features grouping
redundancy analysis
stream of features
streaming data
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