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《Journal of Hubei University(Natural Science)》 2018-01
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Classification of the imbalanced data streaming based on random balanced sampling

YUAN Lei;JI Mengyao;Department of Information Center,Renmin Hospital of Wuhan University;Department of Gastroenterology,Renmin Hospital of Wuhan University;  
The vast majority of real world streaming classification problems is imbalanced and deteriorates the performance to existing classifiers. This work proposed a new experimental data stream sampling algorithm,random balance sampling( RBS), for studying imbalance data stream, and then a new experimental framework,random balance sampling streaming ensemble algorithm( RBSSEA),was built based on RBS to address the imbalance data stream classification problem. Using the new experimental framework, an evaluation study on synthetic and real-world datasets shows that the new ensemble method is an effective classification model compared to several known methods.
【Fund】: 国家自然科学基金(61401263);; 中央高校基本科研业务费专项资金(302-410500195);; 武汉大学自主科研项目(302-410500195 302-410500195)资助
【CateGory Index】: TP181
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