Learning with limited minority class data
暂无分享,去创建一个
Taghi M. Khoshgoftaar | Jason Van Hulse | Chris Seiffert | Amri Napolitano | Andres Folleco | T. Khoshgoftaar | A. Folleco | J. V. Hulse | Amri Napolitano | C. Seiffert | Chris Seiffert
[1] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[2] Gary M. Weiss. Mining with rarity: a unifying framework , 2004, SKDD.
[3] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[4] M. Maloof. Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .
[5] David W. Aha,et al. Lazy Learning , 1997, Springer Netherlands.
[6] Taghi M. Khoshgoftaar,et al. Experimental perspectives on learning from imbalanced data , 2007, ICML '07.
[7] Robert C. Holte,et al. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , 2003 .
[8] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[9] Claes Wohlin,et al. Experimentation in software engineering: an introduction , 2000 .
[10] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[11] Rosa Maria Valdovinos,et al. The Imbalanced Training Sample Problem: Under or over Sampling? , 2004, SSPR/SPR.
[12] Foster J. Provost,et al. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..
[13] Zhi-Hua Zhou,et al. Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).
[14] Tom Fawcett,et al. Robust Classification for Imprecise Environments , 2000, Machine Learning.