[1]胡小生,钟勇.基于加权聚类质心的SVM不平衡分类方法[J].智能系统学报,2013,8(03):261-265.
 HU Xiaosheng,ZHONG Yong.Support vector machine imbalanced data classification based on weighted clustering centroid[J].CAAI Transactions on Intelligent Systems,2013,8(03):261-265.
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基于加权聚类质心的SVM不平衡分类方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年03期
页码:
261-265
栏目:
出版日期:
2013-06-25

文章信息/Info

Title:
Support vector machine imbalanced data classification based on weighted clustering centroid
文章编号:
1673-4785(2013)03-0261-05
作者:
胡小生钟勇
佛山科学技术学院 电子与信息工程学院,广东 佛山 528000
Author(s):
HU Xiaosheng ZHONG Yong
College of Electronic and Information Engineering, Foshan University, Foshan 528000, China
关键词:
机器学习不平衡数据分类聚类质心支持向量机
Keywords:
machine learning imbalanced data classification clustering centroid support vector machine
分类号:
TP181
文献标志码:
A

参考文献/References:

[1]叶志飞,文益民,吕宝粮.不平衡分类问题研究综述[J].智能系统学报, 2009, 4(2): 148-156.
YE Zhifei, WEN Yimin, L Baoliang. A survey of imbalanced pattern classification problems[J]. CAAI Transactions on Intelligent Systems, 2009, 4(2):148-156.
[2]RONALDO C P, GUSTAVO E A, MARIA C M. A study with class imbalance and random sampling for a decision tree learning system[C]//International Conference for Information Processing. Milano, Italy, 2008: 131-140.
[3]WU Junjie, XIONG Hui, WU Peng, et al. Local decomposition for rare class analysis[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2007: 814-823.
[4]HE Haibo, GARCIA E A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263-1284.
[5]李雄飞,李军,董元方,等.一种新的不平衡数据学习算法PCBoot[J].计算机学报, 2012, 35(2): 203-209.
LI Xiongfei, LI Jun, DONG Yuanfang, et al. A new learning algorithm for imbalanced data—PCBoost[J]. Chinese Journal of Computers, 2012, 35(2): 203-209.
[6]付忠良.不平衡多分类问题的连续AdaBoost算法研究[J].计算机研究与发展, 2011, 48(2): 2326-2333.
FU Zhongliang. Real AdaBoost algorithm for multi-class and imbalanced classification problems[J]. Journal of Computer Research and Development, 2011, 48(2): 2326-2333.
[7]VEROPOULOS K, CAMPBELL C, CRISTIANINI N. Controlling the sensitivity of support vector machines[C]//Proceedings of the International Joint Conference on Artificial Intelligence. San Francisco, USA, 1999: 55-60.
[8]AKBANI R, KWEK S, JAPKOWICZ N. Applying support vetor machines to imbalanced datasets[C]//Proceedings of 15th European Conference on Machine Learning. Pisa, Italy, 2004: 39-50.
[9]WU G, CHANG E Y. KBA: kernel boundary alignment considering imbalanced data distribution[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 786-795.
[10]ERTEKIN S, HUAN J, BOTTON L, et al. Learning on the border: active learning in imbalanced data classification[C]//Proceedings of the ACM Conference on Information and Knowledge Management. Lisbon, Portugal, 2007: 127-136.
[11]HAN Hui, WANG Wenyuan, MAO Binghuan. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]//Proceedings of the International Conference on Intelligence Computing. Hefei, China, 2005: 878-887.
[12]DASKALAKI S, KOPANAS L. Evaluation of classifiers for an uneven class distribution problem[J]. Applied Artificial Intelligence, 2006, 20(5): 381-417.
[13]L Biaoliang, WANG Kaian, UTIYAMA M, et al. A part-versus-part method for massively parallel training of support vector machines[C]//Proceedings of 17th International Joint Conference on Neural Networks. Budapest, Hungary, 2004, 1: 735-740.

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备注/Memo

备注/Memo:
收稿日期:2012-09-27.
网络出版日期:2013-05-15.
基金项目:佛山市科技发展专项资金资助项目(2011AA100061);佛山市产学研专项资金资助项目(2012HC100272);佛山市教育局智能评价指标体系研究项目(DX20120220).
通信作者:胡小生.
E-mail: happyhxs@tom.com.
作者简介:
胡小生,男,1978年生,讲师,主要研究方向为机器学习、数据挖掘、信息检索.
钟勇,男,1970年生,教授,博士,主要研究方向为信息检索、云计算.
更新日期/Last Update: 2013-08-29