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面向油气储层综合评价的TL-SVM模型与方法 被引量:1

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摘要 将支持向量机应用于油气储层综合评价时,由于钻井数据类别分布不平衡,支持向量机对于少数类样本的分类性能较差。分析了支持向量机对于不平衡数据敏感的原因,并与不平衡分类方法TomekLinks结合,提出TL-SVM模型。实验中分别使用了支持向量机和TLSVM模型,结果表明,TL-SVM模型具有更优的分类性能。
出处 《城市地理》 2018年第2X期86-87,共2页 City Geography
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  • 1[18]Schapire R E,Singer Y.Improved boosting algorithms using confidence-rated predictions[J].Machine Learning,1999,37(3):297 -336.
  • 2[19]Fan W,Stolfo S J,Zhang J,et al.AdaCost:misclassification cost-sensitive boosting[C]//Bratko I,Dzeroski S.Proc of the 16th Intern Conf on Meachine Learning.Morgan Kanfmann,1999:97-105.
  • 3[20]Joshi M V,Kumar V,Agarwal R C.Evaluating boosting algorithms to classify rare classes:comparison and improvements[C]// Cercone N,Lin T Y,Wu X.Pro of the 2001 IEEE Intern Conf on Data Mining.Washington DC:IEEE Computer Society Press,2001:257 -264.
  • 4[21]Chawla N V,Japkowicz,Kolcz A.Editorial:special issue on learning from imbalaneed data sets[J].SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets,2004,6(1):1 -6.
  • 5[22]Chawlal N V,Lazarevic A,Hall L O.SMOTEBoost:improving prediction of the minority class in boosting[C]// The 7th European Conf on Principles and Practice of Knowledge Discovery in Databases.Berlin:Springer,2003:107-119.
  • 6[23]He Guoxun,Han Hui,Wang Wenyuan.An over-sampling expert system for learning from imbalaneed data sets[J].Neural Networks and Brain,2005,1:537 -541.
  • 7[25]Tao Ban,Shigeo Abe.Implementing multi-class classifiers by one-class classification methods[C]// 2006 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel.Vancouver,BC:IEEE Press,2006:16 -21,327 -332.
  • 8[26]Sun Y.Cost-sensitive boosting for classification of imbalanced data[D].Canada:University of Waterloo,2007.
  • 9[27]Constantinopoulos C,Likas A.Semi-supervised and active learning with the probabilistic RBF classifier[J].Artificial Neural Networks,2008,71(13):2489-2498.
  • 10[28]Chen C,Liaw A,Breiman L.Using random forests to learn unbalanced data[R].California:University of California,2004.

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