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Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Neighborhood Rough Set
Pages: 726-735
Year: Issue:  8
Journal: Pattern Recognition and Artificial Intelligence

Keyword:  Online Feature SelectionHigh-Dimensional and Class-Imbalance DataNeighborhood Rough SetRough Dependence;
Abstract: In many real world applications, data is dynamically generated at different time periods in addition to high-dimensional imbalanced features. An high-dimensional class-imbalanced online feature selection algorithm based on neighborhood rough set is proposed. The algorithm design is based on rough dependency calculation formula of small class significance. Meanwhile, three evaluation criteria of online relevance analysis, online redundancy analysis and online significance analysis, are presented to select features with high separability between majority and minority classes. Experimental results on seven high-dimensional and class-imbalanced datasets show that the proposed method can effectively select a better feature subset with better performance.
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