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| 基于滑动窗口的快速Learn++.NSE* | |
| 引用本文： | 申彦,朱玉全,宋新平.基于滑动窗口的快速Learn++.NSE*[J].模式识别与人工智能,2017,30(12):1083-1090. |
| 作者姓名： | 申彦 朱玉全 宋新平 |
| 作者单位： | 1.江苏大学 信息管理与信息系统系 镇江 212013 2.江苏大学 计算机科学与通信工程学院 镇江 212013 3.江苏大学 电子商务系 镇江 212013 |
| 基金项目： | 国家自然科学基金项目(No.61702229,71573107)、江苏省自然科学基础研究计划基金项目(No.BK20150531)、江苏省博士后科研资助计划项目(No.1401056C)、全国统计科学研究项目(No.2016LY17)、江苏大学高级人才基金项目(No.13JDG127)资助 |
| 摘 要： | Learn++.NSE集成的单个基分类器需根据其在所有历经环境中的分类错误率加权计算投票权重,学习效率有待提高.因此,文中采用滑动窗口技术优化权重的计算过程,提出基于滑动窗口的快速Learn++.NSE算法(SW-Learn++.NSE).该算法仅考虑使用单个基分类器近期窗口内的分类准确率计算投票权重,提高集成学习的效率.实验表明,相比Learn++.NSE,在取得同等分类准确率的情况下,文中算法分类学习的效率更高. |
| 关 键 词： | 分类算法 大数据挖掘 集成学习 增量学习 |
| 收稿时间： | 2017-05-22 |
Fast Learn++.NSE Algorithm Based on Sliding Window |
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| SHEN Yan,ZHU Yuquan,SONG Xinping.Fast Learn++.NSE Algorithm Based on Sliding Window[J].Pattern Recognition and Artificial Intelligence,2017,30(12):1083-1090. | |
| Authors: | SHEN Yan ZHU Yuquan SONG Xinping |
| Affiliation: | 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 |
| 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. |
| Keywords: | Classification Algorithm Big Data Mining Ensemble Learning Incremental Learning |
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