A Comparative Study of Data Sampling and Cost Sensitive Learning
暂无分享,去创建一个
Taghi M. Khoshgoftaar | Jason Van Hulse | Chris Seiffert | Amri Napolitano | T. Khoshgoftaar | J. V. Hulse | Amri Napolitano | C. Seiffert | Chris Seiffert
[1] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.
[2] Charles Elkan,et al. The Foundations of Cost-Sensitive Learning , 2001, IJCAI.
[3] Salvatore J. Stolfo,et al. AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.
[4] Kai Ming Ting,et al. A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.
[5] Dragos D. Margineantu,et al. Class Probability Estimation and Cost-Sensitive Classification Decisions , 2002, ECML.
[6] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[7] Aiko M. Hormann,et al. Programs for Machine Learning. Part I , 1962, Inf. Control..
[8] Pedro M. Domingos. MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.
[9] David M. Levine,et al. Intermediate Statistical Methods and Applications: A Computer Package Approach , 1982 .
[10] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[11] Gary M. Weiss,et al. Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs? , 2007, DMIN.
[12] Yang Wang,et al. Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..
[13] Zhaohui Wu,et al. Enhancing Reliability throughout Knowledge Discovery Process , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).
[14] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[15] Honghua Dai,et al. A Study on Reliability in Graph Discovery , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).
[16] Robert C. Holte,et al. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , 2003 .