Abstract
In recent years, many online streaming feature selection approaches focus on flat data, which means that all data are taken as a whole. However, in the era of big data, not only the feature space of data has unknown and evolutionary characteristics, but also the label space of data exists hierarchical structure. To address this problem, an online streaming feature selection framework for large-scale hierarchical classification task is proposed. The framework consists of three parts: (1) a new hierarchical data-oriented kernelized fuzzy rough model with sibling strategy is constructed, (2) the online important feature is selected based on feature correlation analysis, and (3) the online redundant feature is deleted based on feature redundancy. Finally, an empirical study using several hierarchical classification data sets manifests that the proposed method outperforms other state-of-the-art online streaming feature selection methods.
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