A sparse Bayesian approach for joint feature selection and classifier learning

Pattern Analysis and Applications

Volumn 11, Issue 3-4, 2008, Pages 299-308

  Lapedriza, Àgata ^a   Seguí, Santi ^a   Masip, David ^a,b   Vitrià, Jordi ^a,c  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

^b UNIVERSITAT OBERTA DE CATALUNYA   (Spain)

^c UNIVERSITY OF BARCELONA   (Spain)

Author keywords

Bayesian learning; Classification; Feature selection

Indexed keywords

EID: 50549087032     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-008-0130-1     Document Type: Article

References (41)

1. Bellman R (1961) Adaptive control process: a guided tour. Princeton University Press, New Jersey
2. Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Wiley
3. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
4. Schapire RE, Freund Y, Bartlett PL, Lee WS (1997) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):322-330
5. Madigan D, Genkin A, Lewis DD, Fradkin D (2005) Bayesian multinomial logistic regression for author identification. In: AIP conference proceedings-25th international workshop on Bayesian inference and maximum entropy methods in science and engineering, vol 803, pp 509-516, 23 November 2005
6. Abe N, Kudo M, Toyama J, Shimbo M (2006) Classifier-independent feature selection on the basis of divergence criterion. Pattern Anal Appl 9(2-3):127-137
7. Zivkovic Z, van der Heijden F (2004) Improving the selection of feature points for tracking. Pattern Anal Appl 7(2):144-150
8. Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153-158
9. Fisher R (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7:179-188
10. Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, Boston
11. Masip D, Kuncheva LI, Vitria J (2005) An ensemble-based method for linear feature extraction for two-class problems. Pattern Anal Appl 8:227-237
12. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182
13. Guyon I, Weston J, Barnhill S, Vapnik V (2004) Gene selection for cancer classification using support vector machines. Mach Learn 46(1-3):389-422
14. Mao KZ (2004) Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Trans Syst Man Cybern Part B 34(1):60-67
15. Chen S, Wang X, Hong X, Harris CJ (2006) Kernel classifier construction using orthogonal forward selection and boosting with fisher ratio class separability measure. IEEE Trans Neural Netw 17(6):1652-1656
16. Hong X, Mitchell RJ (2007) Backward elimination model construction for regression and classification using leave-one-out criteria. Int J Syst Sci 38(2):101-113
17. Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V (2000) Feature selection for SVMs. In: Leen TK, Dietterich TG, Tresp V (eds) NIPS. MIT Press, Cambridge, pp 668-674
18. Neal RM (1996) Bayesian learning for neural networks. LNS, vol 118. Springer, Heidelberg
19. Seeger M (1999) Bayesian model selection for support vector machines, gaussian processes and other kernel classifiers. In: Solla SA, Leen TK, Müller K-R (eds) NIPS. The MIT Press, Cambridge, pp 603-609
20. Zhu J, Rosset S, Hastie T, Tibshirani R (2004) 1-Norm support vector machines. In: Thrun S, Saul L, Schölkopf B (eds) Advances in Neural information processing systems, vol 16. MIT Press, Cambridge
21. Jebara T, Jaakkola T (2000) Feature selection and dualities in maximum entropy discrimination. In: Proc. 16th conf. on uncertainty in artif. intell. Morgan Kaufmann Publ. Inc, San Francisco, pp 291-300
22. Li K, Peng J, Bai E (2006) A two-stage algorithm for identification of nonlinear dynamic systems. Automatica 42(7):1189-1197
23. Krishnapuram B, Hartemink AJ, Carin L, Figueiredo MAT (2004) A bayesian approach to joint feature selection and classifier design. IEEE Trans Pattern Anal Mach Intell 26(9):1105-1111
24. Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1:211-244
25. Effron B, Hastie T, Johnstone I, Tibshinrani R (2004) Least angle regression. Ann Stat 32(2):407-499
26. Effron B, Hastie T, Johnstone I, Tibshinrani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58(1):267-288
27. Zhao P, Yu B (2007) Stagewise lasso. J Mach Learn Res 8:2701-2726
28. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
29. Osborne M, Presnell B, Turlach B (2000) A new approach to variable selection in least squares problems. J Numer Anal 20(3):389-403
30. Osborne M, Presnell B, Turlach B (2000) On the lasso and its dual. J Comput Graph Stat 9(2):319-337
31. Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407
32. Vert J-P, Foveau N, Lajaunie C, Vandenbrouck Y (2006) An accurate and interpretable model for sirna efficacy prediction. BMC Bioinf 7:520-537
33. Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K (2005) Sparsity and smoothness via the fused lasso. J R Stat Soc Series B 67(1):91-108
34. Ghosh D, Chinnaiyan A (2005) Classification and selection of biomarkers in genomic data using lasso. J Biomed Biotechnol 2005(2):147-54
35. Gao J, Suzuki H, Yu B (2006) Approximation lasso methods for language modeling. In: ACL '06: proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the ACL, Association for Computational Linguistics. Morristown, NJ, pp 225-232
36. Obozinsky G, Taskar B, Jordan M (2006) Multi-task feature selection. Tech. rep., Statistics Department UC Berkeley
37. Igual L, Seguí S, Vitrià J, Azpiroz F, Radeva P (2007) Sparse bayesian feature selection applied to intestinal motility analysis. In: XVI Congreso Argentino de Bioingeniería, pp 467-470
38. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337-374
39. Blake C, Merz C (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html
40. Pranckeviciene E, Ho T, Somorjai RL (2006) Class separability in spaces reduced by feature selection. In: ICPR (3). IEEE Computer Society, pp 254-257
41. Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature-selection. Pattern Recognit Lett 15(11):1119-1125