[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(3):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(3):261-265.
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HU Xiaosheng,ZHONG Yong.Support vector machine imbalanced data classification based on weighted clustering centroid[J].CAAI Transactions on Intelligent Systems,2013,8(3):261-265.
基于加权聚类质心的SVM不平衡分类方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
8
期数:
2013年第3期
页码:
261-265
栏目:
学术论文—机器学习
出版日期:
2013-06-25
- Title:
- Support vector machine imbalanced data classification based on weighted clustering centroid
- 文章编号:
- 1673-4785(2013)03-0261-05
- 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
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