Type independent correction of sample selection bias via structural discovery and re-balancing

Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

Volumn 2, Issue , 2008, Pages 565-576

  Ren, Jiangtao ^a   Shi, Xiaoxiao ^a   Fan, Wei ^b   Yu, Philip S ^c  

^a SUN YAT SEN UNIVERSITY   (China)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

Clustering; Sample selection bias; Sampling; Structure

Indexed keywords

DATA MINING; DECISION TREES; POPULATION STATISTICS; SAMPLING; STRUCTURE (COMPOSITION);

BASELINE MODELS; CLUSTERING; COVARIATE SHIFTS; DIFFERENT DISTRIBUTIONS; LOGISTIC REGRESSIONS; NATURAL STRUCTURES; NEAREST NEIGHBORS; SAMPLE SELECTION BIAS;

CLASSIFICATION (OF INFORMATION);

EID: 52649174887     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972788.52     Document Type: Conference Paper

References (14)

1. S. Bickel, M. Brückner, T. Scheffer, Discriminative Learning for Differing Training and Test Distributions. In Proceedings of the 24 th International Conference on Machine Learning, ICML 2007.
2. S. Bickel, T. Scheffer, Dirichlet-enhanced spam filter based on biased samples. In advances in Neural Information Processing Systems 19, pages 161-168, MIT Press, 2007.
3. M. Dudik, R. Schapire and S. Phillips, Correcting sample selection bias in maximum entropy density estimation. In Advances of Neural Information Processing Systems Conference, MIT Press, 2005.
4. W. Fan, and I. Davidson, On Sample Selection Bias and Its Efficient Correction via Model Averaging and Unlabeled Examples. In Proceedings of the SIAM International Conference on Data Mining, SDM 2007.
5. W. Fan, I. Davidson, B. Zadronzny and P. Yu, An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias. The 5th IEEE International Conference on Data Mining, ICDM 2005.
6. J. Huang, A. Smola, A. Gretton, K. Borgwardt, and B. Scholkopf, Correcting Sample Selection Bias by Unlabeled Data. In Advances of Neural Information Processing System Conference, MIT Press, 2007.
7. Heckman, J.,Sample selection bias as a specification error. Econometrica, 47: 153-161
8. S. M. Savaresi and D. L. Boley, On the performance of bisecting K-means and PDDP. In Proceedings of the SIAM International Conference on Data Mining, SDM 2001.
9. Chawla N. V. and Karakoulas G., Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains. Journal of Artificial Intelligence and Research, Volume 23, pages 331-366, 2005.
10. Elżbieta PeRkalska, Robert P.W. Duin, Pavel Paclik, Prototype selection for dissimilarity-based classifiers. Journal of Pattern Recognition, Volume39, pages 189-208, 2005.
11. S. Rosset, J. Zhu, H. Zou, and T. Hastie, A method for inferring label sampling mechanisms in semi-supervised learning. In Advances in Neural Information Processing Systems 17, pages 1161-1168, MIT Press, 2005.
12. A. Smith, C. Elkan, Making Generative Classifiers Robust to Selection Bias. The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007.
13. A. Smith, C. Elkan, A Bayesian network framework for reject inference. In Proceedings of Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004.
14. B. Zadrozny,Learning and evaluating classifiers under sample selection bias. The 21th International Conference on Machine Learning, ICML 2004.