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面向高速数据流的偏倚抽样集合分类器

DOI: 10.13190/jbupt.201004.44.zhangjp, PP. 44-48

Keywords: 数据流,集合分类器,偏倚抽样,偏差方差分解

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Abstract:

针对高速数据流的流速超过集合分类器的处理能力,集合分类器无法训练全部最近到达的数据以更新分类器模型的问题,提出一种偏倚抽样集合分类器算法.通过偏差方差分解方法分析集合分类器的期望错误,利用计算待抽样数据的期望错误贡献度,实现数据的偏倚抽样,有效缩减了集合分类器的训练更新时间.与随机抽样集合分类器方法进行了比较.理论分析和实验结果表明,在抽样比例相同的条件下,该方法可以有效提高集合分类器的分类准确率.

References

[1]  Wang H, Fan W, Yu P S, et al. Mining concept-drifting data stream using ensemble classifiers// Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, D. C: ACM, 2003: 226-235.
[2]  Domingos P, Hulten G. Mining high-speed data streams//Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston: ACM, 2000: 71-80.
[3]  Street W N, Kim Y S. A streaming ensemble algorithm for large-scale classification//Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2001: 377-382.
[4]  Chu F, Zaniolo C. Fast and light boosting for adaptive mining of data streams//Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Sydney : Springer Verlag, 2004: 282-292.
[5]  Wei Fan. Systematicdata selection to mine concept-drifting data streams//Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle: ACM, 2004: 128-137.
[6]  Zhang Yi, Jin Xiaoming. An automatic construction and organization strategy for ensemble learing on data streams[J]. ACM SIGMOD Record, 2006, 35(3): 28-33.
[7]  孙悦, 毛国军, 刘旭, 等. 基于多分类器的数据流中的概念漂移挖掘[J]. 自动化学报, 2008, 34(1): 93-96. Sun Yue, Mao Guojun, Liu Xu, et al. Ming concept drift from data streams based on multi-classifiers[J]. ACTA Automatica Sinica, 2008, 34(1): 93-96.
[8]  Tumer K, Ghosh J. Analysis of decision boundaries in linearly combined neural classifiers[J]. Pattern Recognition, 1996, 29(2): 341-348.
[9]  Tumer K, Ghosh J. Error correlation and error reduction in ensemble classifiers[J]. Connection Science, 1996, 8(3-4): 385-404.
[10]  付忠良. 关于AdaBoost有效性分析[J]. 计算机研究与发展, 2008, 45(10): 1747-1755. Fu Zhongliang. Effectiveness analysis of adaboost[J]. Journal of Computer Research and Development, 2008, 45(10): 1747-1755.
[11]  周志华, 王珏. 机器学习机器应用2007[M]. 北京: 清华大学出版社, 2007.

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