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| Fast Learn++.NSE Algorithm Based on Sliding Window |
| SHEN Yan 1, 2, ZHU Yuquan2, SONG Xinping3 |
1.Department of Information Management and Information System, Jiangsu University, Zhenjiang 212013
2.School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013
3.Department of Electronic Commerce, Jiangsu University, Zhenjiang 212013 |
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Abstract The vote weight of each base-classifier in Learn++.NSE depends on all the error rates in the environments experienced, and the classification learning efficiency of the Learn++.NSE needs to be improved. Therefore, a fast Learn++.NSE algorithm based on sliding window(SW-Learn++.NSE) is presented in this paper. The sliding window is utilized to optimize the calculation of the weight. By only using the recent classification error rates of each base-classifier inside the sliding window to compute the vote weight, the SW-Learn++.NSE improves the efficiency of ensemble classification learning greatly. The experiment shows that the SW-Learn++.NSE achieves a higher execution efficiency with an equivalent classification accuracy compared to the Learn++.NSE.
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Received: 22 May 2017
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| Fund:Supported by National Natural Science Foundation of China(No.61702229,71573107), Natural Science Foundation of Jiangsu Province(No.BK20150531), Jiangsu Planned Projects for Postdoctoral Research Funds(No.1401056C), National Statistical Science Research Project of China(No.2016LY17), Advanced Talent Foundation of Jiangsu University(No.13JDG127) |
About author:: (SHEN Yan(Corresponding author), born in 1982, Ph.D., associate professor. His research interests include data mining, business intelligence and intelligent information system.)
(ZHU Yuquan, born in 1965, Ph.D., professor. His research interests include know-ledge discovery, big data mining and complex information system integration and intrusion detection.)
(SONG Xinping, born in 1971, Ph.D., professor. Her research interests include big data mining and business intelligence.) |
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2017, Vol. 30 
