Multiple kernel learning based on local and nonlinear combinations

38th Latin America Conference on Informatics, CLEI 2012 - Conference Proceedings

Volumn , Issue , 2012, Pages

  Calderón Niquín, Marks ^a   Valverde Rebaza, Jorge ^b  

^a UNIVERSIDAD NACIONAL DE TRUJILLO   (Peru)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

kernel machines; microarray classification; microarrays; multiple kernel learning; support vector machines

Indexed keywords

DATA DEPENDENT; GATING MODELS; HIGH-DIMENSIONAL; KERNEL FUNCTION; KERNEL MACHINE; MICROARRAY CLASSIFICATION; MICROARRAY DATA SETS; MULTIPLE KERNEL LEARNING; NONLINEAR COMBINATION; OPTIMAL KERNELS; SINGLE KERNEL; SPECIFIC TASKS;

EXPERIMENTS; INFORMATION SCIENCE; MICROARRAYS; OPTIMIZATION;

SUPPORT VECTOR MACHINES;

EID: 84874334409     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CLEI.2012.6427179     Document Type: Conference Paper

References (35)

1. P. J. G. Lisboa, A. Velido, R. Tagliaferri, F. Napolitano, M. Ceccarelli, J. D. Martín-Guerrero, and E. Biganzoli, "Application notes: data mining in cancer research," Comp. Intell. Mag., vol. 5, no. 1, pp. 14-18, Feb. 2010.
2. N. Ainhoa Perez Diez, Andrey Morgun, Microarrays for Cancer Diagnosis and Classification, ser. Advances in Experimental Medicine and Biology, S. Mocellin, Ed. Springer, 2007.
3. K. Cobb, "Microarrays the search for meaning in a vast sea of data," Biomedical Computation Review Fall, pp. 16-23, 2006.
4. J. P. Mehta, "Gene expression analysis in breast cancer," Ph.D. dissertation, Dublin City University, Ireland, 2010.
5. S. Knudsen, Guide to Analysis of DNA Microarray data, 2nd ed. Wiley-Liss, 2004.
6. S. Seeja.K.R, "Microarray data classification using support vector machine," International Journal of Biometrics and Bioinformatics, (IJBB), vol. 5, pp. 10-15, 2011.
7. P. F. Macgregor and J. A. Squire, "Application of microarrays to the analysis of gene expression in cancer," Clinical Chemistry, vol. 48, no. 8, pp. 1170-1177, 2002.
8. Y. Wang, J. G. M. Klijn, Y. Zhang, A. M. Sieuwerts, M. P. Look, F. Yang, D. Talantov, M. Timmermans, M. E. Meijer-van Gelder, J. Yu, T. Jatkoe, E. M. Berns, D. Atkins, and J. A. Foekens, "Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer," The Lancet, vol. 365, no. 9460, pp. 671-679, 2005.
9. S. Mukherjee, Classifying microarray data using Support Vector Machines. Kluwer Academic Publishers, 2003, ch. 9.
10. M. Gönen and E. Alpaydin, "Multiple kernel learning algorithms," JMLR, vol. 12, pp. 2211-2268, 2011.
11. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet, "More efficiency in multiple kernel learning," in Proceedings of the 24th international conference on Machine learning, ser. ICML '07. New York, NY, USA: ACM, 2007, pp. 775-782.
12. C. Cortes, M. Mohri, and A. Rostamizadeh, "Learning non-linear combinations of kernels," in NIPS, 2009, pp. 396-404.
13. G. Mehmet and A. Ethem, "Localized multiple kernel learning," in Proceedings of the 25th International Conference on Machine Learning, ser. ICML'08, 2008, pp. 352-359.
14. V. N. Vapnik, Statistical Learning Theory. John Wiley and Sons, Chichester, 1998.
15. M. Gönen, A. Ulaş, P. Schüffler, U. Castellani, and V. Murino, "Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma," in Proceedings of the First international conference on Similarity-based pattern recognition, ser. SIMBAD'11. SpringerVerlag, 2011, pp. 250-260.
16. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the fifth annual workshop on Computational learning theory, ser. COLT '92, 1992, pp. 144-152.
17. G. R. G. Lanckriet, T. De Bie, N. Cristianini, M. I. Jordan, and W. S. Noble, "A statistical framework for genomic data fusion," Bioinformat-ics, vol. 20, no. 16, pp. 2626-2635, Nov. 2004.
18. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, "Large scale multiple kernel learning," JMLR, vol. 7, pp. 1531-1565, 2006.
19. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan, "Multiple kernel learning, conic duality, and the smo algorithm," in Proceedings of the 21th International Conference on Machine Learning, ser. ICML'04, 2004.
20. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, "Learning the kernel matrix with semidefinite programming," JMLR, vol. 5, pp. 27-72, Dec. 2004.
21. H. W. Kuhn, "Nonlinear programming: A historical view," SIGMAP Bull., no. 31, pp. 6-18, Jun. 1982.
22. S. Amari and S. Wu, "Improving support vector machine classifiers by modifying kernal functions," Neural Netw., vol. 12, no. 6, pp. 783-789, Jul. 1999.
23. M. I. Jordan and R. A. Jacobs, "Hierarchical mixtures of experts and the EM algorithm," Neural Comput., vol. 6, no. 2, pp. 181-214, Mar. 1994.
24. E. F. Petricoin, A. M. Ardekani, B. A. Hitt, P. J. Levine, V. A. Fusaro, S. M. Steinberg, G. B. Mills, C. Simone, D. A. Fishman, E. C. Kohn, and L. A. Liotta, "Use of proteomic patterns in serum to identify ovarian cancer," The Lancet, vol. 359, no. 9306, pp. 572-577, Feb. 2002.
25. U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine, "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays," Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 12, pp. 6745-6750, 1999.
26. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander, "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, pp. 531-537, Oct. 1999.
27. L. J. van 't Veer, H. Dai, M. J. van de Vijver, Y. D. He, A. A. M. Hart, M. Mao, H. L. Peterse, K. van der Kooy, M. J. Marton, A. T. Witteveen, G. J. Schreiber, R. M. Kerkhoven, C. Roberts, P. S. Linsley, R. Bernards, and S. H. Friend, "Gene expression profiling predicts clinical outcome of breast cancer," Nature, vol. 415, no. 6871, pp. 530-536, Jan. 2002.
28. L. D. Miller, J. Smeds, J. George, V. B. Vega, L. Vergara, A. Ploner, Y. Pawitan, P. Hall, S. Klaar, E. T. Liu, and J. Bergh, "An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival." Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 38, pp. 13 550-13 555, 2005.
29. Y. Pawitan, J. Bjöhle, L. Amler, A. L. Borg, S. Egyhazi, P. Hall, X. Han, L. Holmberg, F. Huang, S. Klaar, E. T. Liu, L. Miller, H. Nordgren, A. Ploner, K. Sandelin, P. M. Shaw, J. Smeds, L. Skoog, S. Wedrén, and J. Bergh, "Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts." Breast Cancer Res, vol. 7, no. 6, 2005.
30. C. Desmedt, F. Piette, S. Loi, Y. Wang, F. Lallemand, B. Haibe-Kains, G. Viale, M. Delorenzi, Y. Zhang, M. S. S. d'Assignies, J. Bergh, R. Lidereau, P. Ellis, A. L. Harris, J. G. Klijn, J. A. Foekens, F. Cardoso, M. J. Piccart, M. Buyse, C. Sotiriou, and TRANSBIG Consortium, "Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the transbig multicenter independent validation series." Clinical cancer research : an official journal of the American Association for Cancer Research, vol. 13, no. 11, pp. 3207-3214, Jun. 2007.
31. G. Mehmet and A. Ethem. (2008) Localized multiple kernel learning. [Online]. Available: http://users.ics.aalto.fi/gonen/icml08.phpp
32. ∼cjlin/ libsvm//
33. R. C. Gentleman, V. J. Carey, D. M. Bates, and Bolstad. (2004) Bioconductor: open software development for computational biology and bioinformatics. Department of Biostatistical Science, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA. rgentlem@jimmy.harvard.edu. [Online]. Available: http://www.bioconductor.orgg
34. J. Demsar, "Statistical comparisons of classifiers over multiple data sets," JMLR, vol. 7, pp. 1-30, 2006.
35. J. Valverde-Rebaza and A. Lopes, "Link Prediction in Complex Networks Based on Cluster Information," in Advances in Artificial Intelligence-SBIA 2012, ser. Lecture Notes in Computer Science, L. Barros, M. Finger, A. Pozo, G. Gimenz-Lugo, and M. Castilho, Eds. Springer Berlin Heidelberg, 2012, pp. 92-101.