A quadratic mean based supervised learning model for managing data skewness

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 188-198

  Liu, Wei ^a   Chawla, Sanjay ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Convex optimization; Data skewness; Quadratic mean

Indexed keywords

CLUSTERING ALGORITHMS; CONVEX OPTIMIZATION; HIGHER ORDER STATISTICS; MACHINE LEARNING; REGRESSION ANALYSIS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

CONVEX OPTIMIZATION PROBLEMS; DATA SKEWNESS; EMPIRICAL RISK MINIMIZATION; HIGH DIMENSIONAL DATA; LEARNING FRAMEWORKS; LOGISTIC REGRESSIONS; QUADRATIC MEAN; SUPPORT VECTOR REGRESSION (SVR);

DATA MINING;

EID: 84857182086     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.17     Document Type: Conference Paper

References (34)

1. R. Akbani, S. Kwek, and N. Japkowicz. Applying support vector machines to imbalanced datasets. Lecture Notes in Computer Science: ECML 2004, 3201:39-50.
2. A. Asuncion and D.J. Newman. UCI Machine Learning Repository, 2007.
3. C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap. Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem. Advances in Knowledge Discovery and Data Mining (PAKDD'09), 5476:475-482, 2009.
4. O. Chapelle. Training a support vector machine in the primal. Neural Computation, 19(5):1155-1178, 2007.
5. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer. SMOTE: synthetic minority over-sampling. Journal of Artificial Intelligence Research, 16(1):321-357, 2002.
6. N.V. Chawla, N. Japkowicz, and A. Kotcz. Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter, 6(1):1-6, 2004.
7. N.V. Chawla, A. Lazarevic, L.O. Hall, and K.W. Bowyer. SMOTEBoost: Improving prediction of the minority class in boosting. Lecture notes in computer science, pages 107-119, 2003.
8. D.A. Cieslak and N.V. Chawla. Learning Decision Trees for Unbalanced Data. In Lecture Notes of Computer Science: ECML PKDD 2008 Part I, pages 241-256, 2008.
9. M. Collins, R.E. Schapire, and Y. Singer. Logistic regression, AdaBoost and Bregman distances. Machine Learning, 48(1):253-285, 2002.
10. J. Davis and M. Goadrich. The relationship between precision-recall and roc curves. In Proceedings of the 23rd International Conference on Machine Learning, pages 233-240, 2006.
11. J. Demšar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, 2006.
12. R.E. Fan, K.W. Chang, C.J. Hsieh, X.R. Wang, and C.J. Lin. Liblinear: A library for large linear classification. The Journal of Machine Learning Research, 9:1871-1874, 2008.
13. C. Gentile and M.K. Warmuth. Linear hinge loss and average margin. In Proceedings of the 1998 conference on Advances in neural information processing systems II, pages 225-231.
14. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine learning, 46(1):389-422, 2002.
15. E.H. Han and G. Karypis. Centroid-based document classification: Analysis and experimental results. Principles of Data Mining and Knowledge Discovery, pages 116-123, 2000.
16. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2001.
17. W.A. Hendricks and K.W. Robey. The sampling distribution of the coefficient of variation. The Annals of Mathematical Statistics, 7(3):129-132, 1936.
18. J.B. Hiriart-Urruty and C. Lemaréchal. Convex Analysis and Minimization Algorithms: Fundamentals. Springer, 1993.
19. K. Kira and L.A. Rendell. The feature selection problem: Traditional methods and a new algorithm. In Proceedings of the National Conference on Artificial Intelligence, pages 129-129, 1992.
20. R. Koenker. Quantile regression. Cambridge University Press, 2005.
21. S. Köknar-Tezel and L.J. Latecki. Improving svm classification on imbalanced data sets in distance spaces. In Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, pages 259-267, 2009.
22. B. Liu, W. Hsu, Y. Ma, A.A. Freitas, and J. Li. Integrating Classification and Association Rule Mining. IEEE Transactions on Knowledge and Data Engineering, 18:460-471.
23. W. Liu, S. Chawla, D.A. Cieslak, and N.V. Chawla. A Robust Decision Tree Algorithms for Imbalanced Data Sets. In Proceedings of the Tenth SIAM International Conference on Data Mining, pages 766-777, 2010.
24. K.M. McGreevy, S.R. Lipsitz, J.A. Linder, E. Rimm, and D.G. Hoel. Using median regression to obtain adjusted estimates of central tendency for skewed laboratory and epidemiologic data. Clinical chemistry, 55(1):165-169, 2009.
25. M. Seeger, C.K.I. Williams, and N.D. Lawrence. Fast forward selection to speed up sparse Gaussian process regression. In Workshop on AI and Statistics, volume 9.
26. C.H. Teo, SVN Vishwanthan, A.J. Smola, and Q.V. Le. Bundle methods for regularized risk minimization. Journal of Machine Learning Research, 11:311-365, 2010.
27. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267-288, 1996.
28. V. Vapnik, S.E. Golowich, and A. Smola. Support vector method for function approximation, regression estimation, and signal processing. Advances in neural information processing systems, pages 281-287, 1997.
29. F. Verhein and S. Chawla. Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets. In Seventh IEEE International Conference on Data Mining, pages 679-684, 2007.
30. C.K.I. Williams. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. Learning in graphical models, pages 599-621, 1997.
31. I.H. Witten and E. Frank. Data mining: practical machine learning tools and techniques with Java implementations. ACM SIGMOD Record, 31(1):76-77, 2002.
32. S.H. Wu, K.P. Lin, C.M. Chen, and M.S. Chen. Asymmetric support vector machines: low false-positive learning. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 749-757, 2008.
33. Q. Yang and X.D. Wu. 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making, 5(4):597-604, 2006.
34. K. Yu, Z. Lu, and J. Stander. Quantile regression: applications and current research areas. Journal of the Royal Statistical Society, 52(3):331-350.