Novel Selective Support Vector Machine Ensemble Learning Algorithm

唐耀华; 高静怀; 包乾宗

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Novel Selective Support Vector Machine Ensemble Learning Algorithm

更新时间：2025-12-08

- Novel Selective Support Vector Machine Ensemble Learning Algorithm
- Vol. 42, Issue 10, Pages: 1221-1225(2008)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Online First：10 October 2008，
  
  Published：2008
- 稿件说明：
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唐耀华, 高静怀, 包乾宗. Novel Selective Support Vector Machine Ensemble Learning Algorithm[J]. 2008, 42(10): 1221-1225.
DOI：

唐耀华, 高静怀, 包乾宗. Novel Selective Support Vector Machine Ensemble Learning Algorithm[J]. 2008, 42(10): 1221-1225. DOI：

摘要

针对支持向量机(SVM)在应用于集成学习中会失效的问题

提出一种选择性SVM集成学习算法(SE-SVM)

利用ξα误差估计法估计个体SVM泛化性度量

并基于负相关学习理论引入差异性度量

通过递归删除法选择出一组泛化性能优良、相互间差异性大的SVM参与集成学习.基于UCI数据的仿真实验表明

SE-SVM能够平均提高SVM的分类正确率0.4%

比常规的Bagging集成学习方法和负相关集成学习方法的分类正确率分别提高了0.24%和0.16%.

Abstract

Focusing on the problem that conventional ensemble learning methods may be invalid when support vector machine(SVM)is used as component learner

a new selectiveSVM ensemble algorithm is proposed. ξα estimator is used to estimate the generalization performance of the component SVM

and negative learning theory is used to introduce diversity among component SVMs. A set of component SVMs with high generalization performance and high diversity is selected during ensemble through recursive elimination algorithm. Experimental results on UCI data sets show that compared with single SVM

conventional Bagging ensemble method and negative learning ensemble method

the classification accuracy of selective SVM ensemble is increased on average by 0.4%

0.24% and 0.16%

respectively.

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references

KIM H C, PANG S, JE H M, et al. Constructing support vector machine ensemble [J]. Pattern Recognition, 2003, 36(12):2757-2767.

VALENTINI G. An experimental bias-variance analysis of SVM ensembles based on resampling techniques [J]. IEEE Transaction on Systems, Man and Cybernetics, 2005, 35(6):1252-1271.

TANG E K, SUGANTHAN P N, YAO X. An analysis of diversity measures [J]. Machine Learning, 2006, 65(1): 247-271.

KROGH A, VEDELSBY J. Neural network ensembles, cross-validation, and active learning[C]∥TOURETZKY D, LEEN T. Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 1995:231-238.

JOACHIMS T. Estimating the generalization performance of a SVM efficiently [C]∥Proceedings of ICML 2000. San Francisco, CA,USA:Morgan Kaufniann Publishers Inc., 2000:431-438.

BLAKE C L, MERZ C J. UCI repository of machine learning databases [DB/OL].(1998-01-01)[2007-10-01]. http:∥www.ics.uci.edu/mlearn/MLRepository.html.

ZANDA M, BROWN G, FUMERA G, et al. Ensemble learning in linearly combined classifiers via negative correlation[C]∥Proceedings of MCS2007. Berlin, Germany:Springer-Verlag, 2007:440-449.

ZHOU Zhihua, WU Jianxin, TANG Wei. Ensembling neural networks: many could be better than all [J]. Artificial Intelligence, 2002, 137(1/2):239-263.

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