期刊文献+

随机森林与支持向量机分类性能比较 被引量:69

Comparison on Classification Performance Between Random Forests and Support Vector Machine
下载PDF
导出
摘要 随机森林是一种性能优越的分类器。为了使国内学者更深入地了解其性能,通过将其与已在国内得到广泛应用的支持向量机进行数据实验比较,客观地展示其分类性能。实验选取了20个UCI数据集,从泛化能力、噪声鲁棒性和不平衡分类三个主要方面进行,得到的结论可为研究者选择和使用分类器提供有价值的参考。 Random Forests is an excellent classifier.In order to make Chinese scholars fully understand its performance,this paper compared it with Support Vector Machine widely used in China by means of data experiments to objectively show its classification performance.The experiments,using 20 UCI data sets,were carried out from three main aspects:generalization,noise robustness and imbalanced data classification.Experimental results can provide references for classifiers'choice and use.
作者 黄衍 查伟雄
出处 《软件》 2012年第6期107-110,共4页 Software
关键词 随机森林 支持向量机 分类 Random Forests Support Vector Machine classification
  • 相关文献

参考文献12

  • 1BREIMAN L.Random Forests[J].Machine Learning,2001,45:5-32.
  • 2方匡南,吴见彬,朱建平,谢邦昌.随机森林方法研究综述[J].统计与信息论坛,2011,26(3):32-38. 被引量:643
  • 3VAPNIK V.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag,1995.
  • 4FRANK A,ASUNCION A.UCI Machine Learning Repository[DB/OL].http://archive.ics.uci.edu/ml.
  • 5LIAW A,WIENER M.RandomForest:Breiman and Cut-ler's random forests for classification and regression[CP/OL].http://CRAN.R-project.org/package=randomForest.
  • 6EVGENIA D,KURT H,FRIEDRICH L,et al.E1071:Misc Functions of the Department of Statistics[CP/OL].http://CRAN.R-project.org/package=e1071.
  • 7CHANG C C,LIN C J.LIBSVM:A Library for Support Vector Machines[J].ACM Transactions on Intelligent Sys-tems and Technology,2011,2(3):27:1-27:27.
  • 8KEERTHI S S,LIN C J.Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel[J].Neural Computa-tion,2003,15(7):1667-1689.
  • 9LIN H T,LIN C J.A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods[R].Department of Computer Science,National Taiwan University,2003.
  • 10MENKE J,MARTINEZ T R.Using Permutations Instead of Student’s t Distribution for p-values in Paired-Differ-ence Algorithm Comparisons[C].Proceedings of2004IEEE International Joint Conference on Neural Networks2004,2:1331-1335.

二级参考文献56

  • 1张启蕊,张凌,董守斌,谭景华.训练集类别分布对文本分类的影响[J].清华大学学报(自然科学版),2005,45(S1):1802-1805. 被引量:26
  • 2刘微,罗林开,王华珍.基于随机森林的基金重仓股预测[J].福州大学学报(自然科学版),2008,36(S1):134-139. 被引量:8
  • 3林成德,彭国兰.随机森林在企业信用评估指标体系确定中的应用[J].厦门大学学报(自然科学版),2007,46(2):199-203. 被引量:35
  • 4Breiman L.Random forest[J].Machine Learning,2001,45 : 5-32.
  • 5Stolfo S .J Fan D W S,Lee W,et al.Credit card fraud detection using meta-learning:Issues~nd initial resuhs[C]//AAAI-97 Wrokshop on AI Methods in Fraud and Risk Mangement,1997.
  • 6Pednanlt E P D,Rosen B K,Apte C.Handling imbalanced data sets in insurance risk modeling,Technical Report RC-21731[R].IBM Research Report, 2000-03.
  • 7Batista G E A P A,Bazzan A L C.Balancing training data for automated annotation of keywords:A case study[C]//Proe of the Second Brazilian Workshop on Bioinformaties,SBC,2003.
  • 8Kubar M,Matwin S.Addressing the course of imbalanced training sets:One-sided selection[C]//Proceedings of 14th International Conference in Machine Learning,San Francisco,CA,1997:179-186.
  • 9Breiman L,Freidman J.Classification and regression trees [M].[S.l.]: Wadsworth, 1984.
  • 10Liu X Y,Wu J.Exploratory under-sampling for class-imbalance learning[C]//Proceedings of the 6th IEEE International Conference on Data Mining(ICDM'06),Hong Kong,China,2006.

共引文献657

同被引文献636

引证文献69

二级引证文献580

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