A generalized boosting algorithm and its application to two-class chemical classification problem

Analytica Chimica Acta

Volumn 543, Issue 1-2, 2005, Pages 181-191

  He, Ping ^a,b   Fang, Kai Tai ^b   Liang, Yi Zeng ^c   Li, Bo Yan ^c  

^a SICHUAN UNIVERSITY   (China)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

^c CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Bayes optimal decision rule; Binary chemical classification; Boosting; Chemometrics

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DECISION THEORY; ERROR ANALYSIS; PROBLEM SOLVING;

BAYES OPTIMAL DECISION RULE; BOOSTING ALGORITHM; CHEMOMETRICS; CLASSIFICATION ALGORITHM;

ALGORITHMS;

ACCURACY; ALGORITHM; ARTICLE; CHEMOMETRICS; ERROR; METHODOLOGY; PREDICTION; PRIORITY JOURNAL;

EID: 20444425838     PISSN: 00032670     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aca.2005.04.043     Document Type: Article

References (23)

1. G.J. Mclachlan Discriminant Analysis and Statistical Pattern Recognition 1992 John Wiley and Sons New York
2. T.W. Anderson An Introduction to Multivariate Statistical Analysis second ed. 1984 John Wiley and Sons New York
3. J.H. Friedman J. Am. Stat. Assoc. 84 1989 165 175
4. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and Regression Trees, The Wadsworth Statistics/Probability Series 1984 Wadsworth Belmont, CA
5. J.H. Friedman Ann. Stat. 19 1991 1 141
6. B.D. Ripley J. Am. Stat. Assoc. 56 1994 409 456
7. T.B. Blank, and S.D. Brown J. Chemom. 8 1994 391 408
8. B.E. Boser, I.M. Guyon, and V.N. Vapnik A training algorithm for optimal margin classifier Proceedings of the Fifth ACM Workshop on Computational Learning Theory Pittsburg, PA 1992 144 152
9. C. Cortes, and V. Vapnik Support vector network Mach. Learn. 20 1995 1 25
10. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag
11. Y. Freund, and R. Schapire Experiments with a new boosting algorithm Proceedings of the 13th International Conference on Machine Learning, 1996 Lorenza Saitta, Morgan Kaufmann, San Francisco 1996 148 156
12. Y. Freund, and R. Schapire A decision theoretic generalized on-line learning and an application to boosting J. Comput. Syst. Sci. 55 1997 119 139
13. P. He, C.J. Xu, Y.Z. Liang, and K.T. Fang Improving the classification accuracy in chemistry via boosting technique Chemom. Intell. Lab. Syst. 70 2004 39 46
14. L. Breiman Mach. Learn. 26 1996 123 140
15. R.O. Duda, and P.E. Hart Pattern Classification and Scene Analysis 1973 John Wiley and Sons New York
16. J.O. Berger Statistical Decision Theory and Bayesian Analysis second ed. 1985 Springer-Verlag New York
17. S.J. Press Bayesian Statistics: Principles, Models, and Applications 1989 John Wiley and Sons Inc. New York
18. J.H. Friedman, T. Hastie, and R. Tibshirani Additive logistic regression: a statistical view of boosting Ann. Stat. 28 2000 307 337
19. D. Coomans, M. Jonckheer, D.L. Massart, I. Broeckaert, and P. Blockx The application of linear discriminant analysis in the diagnosis of thyroid disease Anal. Chim. Acta 103 1978 409 415
20. H. Yoshida, R. Leardi, K. Funatsu, and K. Varmuza Feature selection by genetic algorithms for mass spectral classifiers Anal. Chim. Acta 446 2001 485 494
21. K. Varmuza, and W. Werther J. Chem. Inf. Comput. Sci. 36 1996 323 336
22. K. Varmuza J.C. Lindon G.E. Tranter J.L. Holmes Encyclopedia of Spectroscopy and Spectrometry 2000 Academic Press London 232 243
23. T. Hastie, T.R. Tibshirani, and J.H. Friedman The Elements of Statistical Learning-Data Mining, Inference and Prediction 2001 Springer New York