Classification via mathematical programming survey

Applied and Computational Mathematics

Volumn 8, Issue 1, 2009, Pages 23-35

  Pardalos, Panos M ^a   Erhun Kundakcioglu, O ^a  

^a University of Florida   (United States)

Author keywords

Consistent biclustering; Convex optimization; Integer programming; Mathematical programming; Support vector machines

Indexed keywords

EID: 70249123526     PISSN: 16833511     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (51)

1. Alexe, S., Blackstone, E., Hammer, P., Ishwaran, H., Lauer, M. and Snader, C. Coronary risk prediction by logical analysis of data. Annals of Operations Research, 119:15-42, 2003.
2. Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M. and Yakhini, Z. Tissue classification with gene expression profiles. In RECOMB '00: Proceedings of the fourth annual international conference on Computational molecular biology, pages 54-64, New York, NY, USA, 2000. ACM Press.
3. Ben-Dor, A., Friedman, N. and Yakhini, Z. Class discovery in gene expression data. In RECOMB '01: Proceedings of the fifth annual international conference on Computational biology, pages 31-38, New York, NY, USA, 2001. ACM Press.
4. Bennet, K. and Campbell, C. Support vector machines: Hype or hallelujah? SIGKDD Explorations, 2(2):1-13,2000.
5. Bertsimas, D. and Shioda, R. Classification and regression via integer optimization. Operations Research, 55(2):252-271, 2007.
6. Christopher, M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 2006.
7. Brown, M., Grundy, W., Lin, D., Cristianini, N., Sugne, C., Furey, T., Ares, M. and Haussler, D. Knowledge-base analysis of microarray gene expressiondata by using support vector machines. PNAS, 97(1):262-267, 2000.
8. Bryan, K. Biclustering of expression data using simulated annealing. In CBMS '05: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05), pages 383-388, Washington, DC, USA, 2005. IEEE Computer Society.
9. Busygin, S., Boyko, N., Pardalos, P.M., Bewernitz, M. and Ghacibeh, G. Biclustering EEG data from epileptic patients treated with vagus nerve stimulation. In Onur Seref, O. Erhun Kundakcioglu, and Panos M. Pardalos, editors, Data mining, systems analysis and optimization in biomedicine, volume 953, pages 220-231. American Institute of Physics, 2007.
10. Busygin, S., Prokopyev, O. and Pardalos, P.M. Biclustering in data mining. Computers & Operations Research, 35(9):2964-2987, 2008.
11. Busygin, S., Prokopyev, O. A. and Pardalos, P.M. Feature selection for consistent biclustering. Journal of Combinatorial Optimization, 10:7-21, 2005.
12. Cheng Y. and Church G.M. Biclustering of expression data. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pages 93-103. AAAI Press, 2000.
13. Cifarelli, C. and Patrizi, G. Solving large protein folding problem by a linear complementarity algorithm with 0-1 variables. Optimization Methods and Software, 22(1):25-49, 2007.
14. Cristianini, N. and Shawe-Taylor, J. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK, 2000.
15. Demyanova, V.V. The principal expert method in data mining. Appl.Comput. Math., 4(1):70-74, 2005.
16. Dhillon, I.S. Co-clustering documents and words using bipartite spectral graph partitioning. In KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 269-274, New York, NY, USA, 2001. ACM Press.
17. Dhillon, I.S., Mallela, S. and Modha, D.S. Information-theoretic co-clustering. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 89-98, New York, NY, USA, 2003. ACM Press.
18. Garcia, G.N., Ebrahimi, T. and Vesin, J.M. Joint time-frequency-space classification of eeg in a brain-computer interface application. Journal on Applied Signal Processing, pages 713-729, 2003.
19. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P. Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D. and Lander, E.S. Molecular classification of cancer: Class discovery and class prodiction by gene expression monitoring. Science, 286(5439):531-537, 1999.
20. Hartigan, J.A. Direct clustering of a data matrix. Journal of the American Statistical Association, 67(337):123-129, 1972.
21. Hsu, C.W., Chang, C.C. and Lin, C.J. A practical guide to support vector classification. http://www.csie.ntu.edu.tw/cjlin/papers/guide/guide.pdf, 2004.
22. Huang, Z., Chen, H., Hsu, C.J., Chenb, W.H. and Wuc, S. Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems, 37:543-558, 2004.
23. Joachims, T. Text categorization with support vector machines: Learning with many relevant features. In Claire Nédellec and Céline Rouveirol, editors, Proceedings of the European Conference on Machine Learning, pages 137-142, Berlin, 1998. Springer.
24. Joachims, T. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-184, Cambridge, MA, 1999. MIT Press.
25. Kluger, Y., Basri, R., Chang, J.T. and Gerstein, M. Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res, 13(4):703-716, April 2003.
26. Kundakcioglu, O.E. and Pardalos, P.M. The complexity of feature selection for consistent biclustering, pages 257-266. World Scientific, 2009.
27. Kundakcioglu, O.E., Seref, O. and Pardalos, P.M. Multiple instance learning via margin maximization. submitted, 2009.
28. Lal, T.N., Schroeder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N. and Sch, B.ölkopf. Support vector channel selection in bci. IEEE Transactions on Biomedical Engineering, 51(6):1003-1010, 2004.
29. Lee, E.K. Optimization in Medicine, chapter Optimization-based predictive models in medicine and biology, pages 127-151. Springer, 2008.
30. Lee, E.K. and Wu, T.L. Data Mining, Systems Analysis And Optimization In Biomedicine, chapter Classification and disease prediction via mathematical programming, pages 1-42. American Institute of Physics, 2007.
31. Lee, S. and Verri, A. Pattern recognition with support vector machines. In SVM 2002, Niagara Falls, Canada, 2002. Springer.
32. Madeira, S. and Oliveira, A. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1(1):24-45, 2004.
33. Mammadov, M., Rubinov, A. and Yearwood, J. Data Mining in Biomedicine, volume 7 of Optimization and Its Applications, chapter An optimization approach to identify the relationship between features and output of a multi-label classifier, pages 141-167. Springer, 2007.
34. Mangasarian, O.L. and Wild, E.W. Multisurface proximal support vector classification via generalized eigen-values. Technical Report 04-03, Data Mining Institute, September 2004.
35. Nahapetyan, A., Busygin, S. and Pardalos, P.M. An improved heuristic for consistent biclustering problems. In Mathematical Modelling of Biosystems, Applied Optimization, pages 185-198. Springer, 2008.
36. Noble, W.S. Kernel Methods in Computational Biology, chapter Support vector machine applications in computational biology, pages 71-92. MIT Press, 2004.
37. Osuna, R.F.E. and Girosi, F. An improved training algorithm for support vector machines. In IEEE Workshop on Neural Networks for Signal Processing, pages 276-285, 1997.
38. Pardalos, P.M. and Chinchuluun, A. Some recent developments in deterministic global optimization. Appl.Comput. Math., 5(1):16-34, 2006.
39. Pardalos, P.M. and Romeijn, E. editors. Handbook of Optimization in Medicine. Springer, 2009.
40. Platt, J. Advances in Kernel Methods: Support Vector Learning, chapter Fast training of SVMs using sequential minimal optimization, pages 185-208. MIT press, Cambridge, MA, 1999.
41. Rubinov, A.M. Methods for global optimization of nonsmooth functions with applications (survey). Appl.Comput. Math., 5(1):3-15, 2006.
42. Sch, B. ölkopf and Smola, A. J. Learning with Kernels. MIT Press, Cambridge, 2002.
43. Seref, O., Kundakcioglu, O.E. and Pardalos, P.M. Selective linear and nonlinear classification. In P. M. Pardalos and P. Hansen, editors, CRM Proceedings and Lecture Notes, volume 45, pages 211-234, 2008.
44. Seref, O., Kundakcioglu, O.E., Prokopyev, O.A. and Pardalos, P.M. Selective support vector machines. Journal of Combinatorial Optimization, 17(1):3-20, 2009.
45. Shawe-Taylor, J. and Cristianini, N. Kernel methods for pattern analysis. Cambridge University Press, Cambridge, UK, 2004.
46. Sheng, Q., Moreau, Y. and DeMoor, B. Biclustering microarray data by Gibbs sampling. Bioinformatics, 19:196-205, 2003.
47. Trafalis, T.B. and Ince, H. Support vector machine for regression and applications to financial forecasting. In International Joint Conference on Neural Networks (IJCNN'02), Como, Italy, 2002. IEEE-INNS-ENNS.
48. Vapnik, V. The Nature of Statistical Learning Theory. Springer-Verlag, 1995.
49. Vapnik, V. Statistical Learning Theory. Wiley, New York, 1998.
50. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T. and Vapnik, V. Feature selection for SVMs. In NIPS, pages 668-674, 2000.
51. Xing, E.P. and Karp, R.M. CLIFF: Clustering of high-dimensional microarray data via iterative feature filtering using normilized cuts. Bioinformatics Discovery Note, 17:306-315, 2001.