期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

交叉验证容噪分类算法有效性分析及其在数据流上的应用 被引量：3

Effectiveness Analysis and Application in Data Streams of Cross Validation Noise-Tolerance Classification Algorithm
在线阅读 下载PDF
导出
摘要 交叉验证容噪分类算法是处理含噪音数据集分类问题的重要手段之一.从样本复杂度理论出发,对其有效性进行了详细的理论证明,并给出适用条件.提出一种容噪数据流集合分类算法,理论分析和实验验证表明,该算法与传统交叉验证容噪算法相比,具有更高的分类准确率. Cross validation noise-tolerance classification algorithm is an important method which deals with noisy data set classification problem.According to sample complexity theory,cross validation noise-tolerance classification algorithm validity was proved and applied conditions was given.And noise-tolerance data stream ensemble classifiers was proposed in this paper.Theory and experiment indicated,in contrast with tradition cross validation noise-tolerance classification algorithm,this method had more prediction accuracy.
作者 张健沛 杨显飞 杨静
机构地区 哈尔滨工程大学计算机科学与技术学院
出处 《电子学报》 EI CAS CSCD 北大核心 2011年第2期378-382,共5页 Acta Electronica Sinica
基金 国家自然科学基金(No.60873037 No.61073041 No.61073043)
关键词 交叉验证 容噪 分类 集合分类器 cross validation noise-tolerance classification ensemble classifiers
分类号 TP301 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献16

  • 1X Q Zlau,X D Wu. Class noise vs attribute noise: a quantitative study of their impacts[ J]. Artificial Intelligence Review, 2004, 11(3) : 177 - 210.
  • 2D Shen,Q Yang,Z Chen.Noise reduction through summariza tion for web-page classification[ J]. Information Processing and Management, 2007,43(6) : 1735 - 174.
  • 3V Eglin, S Bres, C Rivero. Hcrmite and Gabor transforms for noise reduction and handwriting classification in ancient manuscripts[ J]. International Journal on Document Analysis and Recognition,2007,9(2) : 101 - 122.
  • 4X D Wu, X Q Zhu. Mining with noise knowledge:error aware data mining[ J]. IEEE. Transactions on Systems Man and Cy bernetics,2008,38(4) :917 - 932.
  • 5G H John. Robust decision trees: Removing outliers from databases[ A]. Proceedings of the First International Confer ence on Knowledge Discovery and Data Mining [ C ]. Menlo Park, CA: AAAI Press, 1995. 174 - 179.
  • 6D Gamgerber, N Lavrac, C Groselj. Experiments with noise fil tering in a medical domain[ A ]. Proceedings of the Sixteenth International Conference on Machine 1.gaming[ C]. San Fran cisco,USA:Morgan Kauffnann, 1999.143 - 151.
  • 7X Q Zhu, X D Wu, Q J Chen. Eliminating class noise in large datasets[ A]. Proceedings of the Twentieth International Confer ence on Machine Learning [ C]. Washington DC, USA: AAAI Press, 2003.920 - 927.
  • 8V Sofie, V A Anneleen. Ensemble methods for noise elimina- lion in classification problems[ A]. Fourth Workshop on Multi ple Classifier Systems [ C ]. United Kingdom: Springer-Verlag Press,2003.317 - 325.
  • 9S Verbaeten. Identifying mislabeled training examples in ILP classification problems[ A] .Proceedings of the Twelfth Dutch Belgian Conference on Machine Learning [ C]. Netherlands: Utrecht of University, 2002.71 - 78.
  • 10J R Quinlan. Induction of decision trees[ J ]. Machine Learn ing, 1986, 1(1) :81 - 106.

二级参考文献20

  • 1H Wang, et al. Mining concept-drifting data streams using ensemble classifiers[ A ]. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C] .New York: ACM Press,2003.226- 235.
  • 2M Scholz, R Klinkenberg. An ensemble classifier for drifting concepts[ A]. Proceedings of the Second International Work- shop on Knowledge Discovery in Data Streams [ C]. Porto, Portugal: Springer,2005.53 - 64.
  • 3Wei Fan. Systematic data selection to mine concept - drifting data streams[A]. Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C] .New York: ACM Press,2004. 128- 137.
  • 4J Z Kolter, M A Maloof. Using additive expert ensembles to cope with concept drift [ A]. Proceedings of the 22nd International Conference on Machine Learning[C]. New York: ACM Press, 2005.449 - 456.
  • 5G M Weiss, F Provost. Learning when training data are costly: the effect of class distribution on tree induction[ J]. JOUlllal of Artificial Intelligence Research, 2003, (19) : 315 - 354.
  • 6N V Chawla, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, (16) :321 - 357.
  • 7G M Weiss. Mining with rarity: a unifying framework[ J]. ACM SIGKDD Explorations, 2004,6( 1 ) :8 - 19.
  • 8C Elkan. The foundations of cost - sensitive learning[A]. Proceedings of the 17th International Joint Conference on Artificial Intelligence[C]. Seattle, Washington, USA: Morgan Kaufinann Publishers Inc, 2001. 973 - 978.
  • 9M Ciraco, M Rogalewski, G Weiss. Improving classifier utility by altering the misclassification cost ratio[A]. Proceedings of the 1st International Workshop on Utility-based Data Mining [C] .New York: ACM Press,2005.46- 52.
  • 10C X Ling, V S Sheng. Cost-sensitive learning and the class imbalance problem [ A ]. Encyclopedia of Machine Learning M]. New York: Springer. 2008.

共引文献22

同被引文献45

  • 1贺益君,高华,陈钟秀.基于基团贡献神经网络集成法估算有机物常压凝固点[J].化工学报,2004,55(7):1124-1130. 被引量：6
  • 2余颖,乔俊飞.活性污泥法污水处理过程的建模与仿真技术的研究[J].信息与控制,2004,33(6):709-713. 被引量：14
  • 3卿晓霞,龙腾锐,王波,余建平.粗集理论在污水参数软测量中的应用研究[J].仪器仪表学报,2006,27(10):1209-1212. 被引量：7
  • 4罗南华,王伟.基于动态递归模糊神经网络的水轮发电机组建模[J].控制与决策,2007,22(10):1125-1128. 被引量：4
  • 5Chu Libing, Yan Sangtian, Xing Xinhui.Progress and perspectives of sludge ozonation as a powerful pretreatment method for minimization of excess sludge production[J].Water Research, 2009, 43 (7):1811-1822.
  • 6Soltysik Dagna, Bednarek Ilona, Loch Tomasz.Repetitive extragenic palindromic PCR (REP-PCR) as a method used for bulking process detection in activated sludge[J].Environmental Monitoring and Assessment, 2011, 176 (1/2/3/4):343-354.
  • 7Han Honggui, Qiao Junfei.Prediction of activated sludge bulking based on a self-organizing RBF neural network[J].Journal of Process Control, 2012, 22 (6):1103-1112.
  • 8Yuan Xichun, Han Honggui, Qiao Junfei.The sludge volume index soft sensor model based on PCA-ElmanNN//IEEE International Joint Conference on Neural Networks[C].Brisbane, Australia, 2012:1-7.
  • 9Mauricio da Motta, Marie-Noёlle Pons, Nicolas Roch, Hervé Vivier.Characterisation of activated sludge by automated image analysis[J].Biochemical Engineering Journal, 2001, 9 (3):165-173.
  • 10Mesquita D P, Amaral A L, Ferreira E C.Identifying different types of bulking in an activated sludge system through quantitative image analysis[J].Chemosphere, 2011, 85 (4):643-652.

引证文献3

二级引证文献19

电子学报
2011年 第2期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部