Efficient cross validation over skewed noisy data

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2008, Pages 749-756

  Dash, Manoranjan ^a   Hao, Oh Yin ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Classification; Cross validation; Noise (key words); Sampling; Skewed data

Indexed keywords

ACCURACY ESTIMATION; ATTRIBUTE VALUES; CLASSIFICATION; CROSS VALIDATION; CROSS-VALIDATION METHODS; DATA SETS; FRAUD DETECTION; FREQUENCY HISTOGRAMS; NOISE (KEY WORDS); NOISY DATA; POOR PERFORMANCE; PREDICTION ACCURACY; SIMPLE RANDOM SAMPLING; SKEWED DATA; UCI MACHINE LEARNING REPOSITORY;

CONTROL THEORY; CYBERNETICS; DATA HANDLING; INTERACTIVE COMPUTER SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; STIMULATED RAMAN SCATTERING;

CLASSIFICATION (OF INFORMATION);

EID: 69949168470     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2008.4811368     Document Type: Conference Paper

References (11)

1. Kohavi, R., "A study of cross-validation and bootstrap for accuracy estimation and model selection", Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI) 2 (12), 1996, pp. 1137-1143.
2. Perenson, M. J., "Hitachi Introduces 1-Terabyte Hard Drive", [http://www.pcworld.com/article/id, 128400-page, 1/article.html], PC World, 2007.
3. Chawla, N. V., Japkowicz, N. and Kolcz, A., "Editorial: Special Issue on Learning from Imbalanced Data Sets", SIGKDD Explorations Vol 6 (1), 2004.
4. Asuncion, A. & Newman, D. J. (2007). UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science.
5. Brodley, C. and Friedl, M., "Identifying mislabeled training data". Journal of Artificial Intelligence Research 11, 1999, pp. 131-167.
6. Zhu, X., Wu, X. and Chen, S., "Eliminating class noise in large datasets", In Proceedings of the 20th ICML International Conference on Machine Learning, 2003, pp. 920-927.
7. Japkowicz, N., editor, In Proceedings of the AAAI'2000 Workshop on Learning from Imbalanced Data Sets, AAAI Tech Report WS-00-05, AAAI, 2000.
8. Chawla, N. V., Japkowicz, N., and Kolcz, A., editors, In Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Data Sets, 2003.
9. Pearson, R., Goney, G., and Shwaber, J., "Imbalanced clustering for microarray time-series", In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.
10. Provost, F. and Fawcett, T., "Robust classification for imprecise environments", Machine Learning, 42(3), 2001, pp. 203-231.
11. Dacheng, T., Xiaoou, T., Xuelong, L. and Xindong, W., "Asymmetric bagging and random rubspace for support vector machines-based relevance feedback in image retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1,088-1,099, 2006.