Double Window Concept Drift Detection Method Based on Sample Distribution Statistical Test - 文章详情页

作者：

Sun, Zijian (Sun, Zijian.) | Tang, Jian (Tang, Jian.) | Qiao, Junfei (Qiao, Junfei.) (学者：乔俊飞)

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摘要：

Aiming　at　the　problem　of　data　stream　drift　due　to　its　time　attribute,　this　paper　proposes　a　double　window　concept　drift　detection　method　based　on　sample　distribution　statistical　test.　In　this　method,　support　vector　regression　(SVR)　is　used　to　detect　data　anomalies　in　the　outlier　detection　window.　The　Euclidean　distance　between　samples　is　statistically　tested　in　the　distribution　detection　window　to　reflect　the　change　of　data　distribution.　This　method　essentially　explores　the　concept　drift　phenomenon　and　applies　it　to　the　regression　problem.　This　paper　verifies　the　performance　of　this　method　based　on　the　benchmark　data　set　of　concrete　compressive　strength.　©　2019　IEEE.

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作者机构：

[ 1 ] [Sun, Zijian]Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 2 ] [Tang, Jian]Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 3 ] [Qiao, Junfei]Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China

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