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基于基学习器差异度的层次化Bagging集成修剪

Hierarchical Bagging Ensemble Pruning Based on the Diversity of Base Learners
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摘要 本文主要目的是寻找到Bagging的一种快速修剪方法,以缩小算法占用的存储空间、提高运算速度和实现提高分类精度的潜力;还提出一种直接计算基学习器差异度的新选择性集成思想.选择出基学习器集合中对提升其余基学习器差异度能力最强者进行删除,通过层次修剪来加速这一算法.在不影响性能的基础上,新算法能够大幅度缩小Bagging的集成规模;新算法还支持并行计算,其进行选择性集成的速度明显优于GASEN.本文还给出了集成学习分类任务的误差上界. The main objective of this paper is to find a rapid pruning method for Bagging to reduce the storage space needed by the algorithm, speed up the computation process and obtain the potential of improving the classification accuracy. A new idea of selective ensemble is proposed, which computes file diversity of base learners directly. The base learner which has the strongest ability to improve the diversity of other base learners in the base learner set is chosen and deleted, and hierarchical pruning is used to speed up the new algorithm. The new algorithm can greatly reduce the size of the bagging ensemble without performance degradation. It also supports parallel computing and its selective ensemble speed is much faster than that of GASEN （genetic algorithm based on selected ensemble）. The upper bound of classification error of ensemble learning is given.
作者 谢元澄 杨静宇
机构地区 南京理工大学计算机与技术学院
出处 《信息与控制》 CSCD 北大核心 2009年第4期449-454,共6页 Information and Control
关键词 选择性集成 差异度 层次修剪 并行计算 基学习器 个体学习器 selective ensemble diversity hierarchical pruning parallel computation base learner component learner
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献13

  • 1Dietterich T G. Experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization[J]. Machine Learning, 2000, 40(2): 139-157.
  • 2Martinez-Munoz G, Suarez A. Switching class labels to generate classification ensembles[J]. Pattern Recognition, 2005, 38(10): 1483-1494.
  • 3Martinez-Munoz G, Suarez A. Using boosting to prune bagging ensembles[J]. Pattern Recognition Letters, 2007, 28(1): 156-165.
  • 4Zhou Z H, Wu J X, Tang W. Ensembling neural networks: Many could be better than all[J]. Artificial Intelligence, 2002, 137(1- 2): 239-263.
  • 5Zhou Z H, Tang W. Selective Ensemble of Decision Trees[R]. Nanjing: Nanjing University, 2003.
  • 6Giacinto G, Roli E An approach to the automatic design of multiple classifier systems[J]. Pattern Recognition Letters, 2001, 22(1): 25-33.
  • 7李凯,黄厚宽.一种基于聚类技术的选择性神经网络集成方法[J].计算机研究与发展,2005,42(4):594-598. 被引量：24
  • 8Tamon C, Xiang J. On the boosting pruning problem[A]. Proceedings of the 1 lth European Conference on Machine Leaming[C]. Berlin, Germany: Springer, 2000. 404-412.
  • 9Breiman L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
  • 10Krogh A, Vedelsby J. Neural network ensembles, cross validation, and active learning[A]. Advances in Neural Information Processing Systems 7[M]. Cambridge, MA, USA: MIT Press, 1995. 231-238.

二级参考文献20

  • 1L.K. Hansen, P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Machine Intelligence, 1990, 12(10): 993～1001.
  • 2P. Sollich, A. Krogh. Learning with ensembles: How over-fitting can be useful. In: D. Touretzky, M. Mozer, M. Hasselmo, eds.Advances in Neural Information Processing Systems, Vol 8.Cambridge, MA: MIT Press, 1996. 190～196.
  • 3L. Breiman. Bagging predictors. Machine Learning, 1996, 24(2): 123～140.
  • 4Y. Freund, R. Schapire. Experiments with a new boosting algorithm. In: Proc. the 13th Int'l Conf. Machine Learning.Bari, Italy: Morgan Kaufmann, 1996.
  • 5A. Krogh, J. Vedelsby. Neural network ensembles, cross validation, and active learning. In: G. Tesauro, D. S.Touretzky, T. K. Leen, eds. Advances in Neural Information Processing Systems 7. Cambridge, MA: MIT Press, 1995. 231～238.
  • 6T. Dietterich, G. Bakin. Solving multiclass learning problems via error-correcting output codes. Journal of AI Research, 1995, 2,263～ 286.
  • 7N. C. Oza, K. Tumer. Dimensionality reduction through classifier ensembles. NASA Ames Research Center, Tech. Rep.:NASA-ARC- IC-1999-126, 1999.
  • 8N. C. Oza, K. Tumer. Input decimation ensembles:Decorrelation through dimensionality reduction. In: J. Kittler, F.Roli, eds. Multiple Classifier Systems. Second InternationalWorkshop (MCS 2001), LNCS 2096. Berlin: Springer, 2001.238～ 247.
  • 9Z. Zheng, G. Webb. Integrating boosting and stochastic attribute selection committees for future improving the performance of decision tree learning. The 10th IEEE ICTAI, Los Alamitos,1998.
  • 10Z.H. Zhou, J. X. Wu, W. Tang. Ensembling neural networks:Many could be better than all. Artificial Intelligence, 2002, 137(1-2): 239～263.

共引文献23

信息与控制
2009年 第4期

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