Constructing support vector machine ensembles for cancer classification based on proteomic profiling

Genomics, Proteomics and Bioinformatics

Volumn 3, Issue 4, 2005, Pages 238-241

  Mao, Yong ^a   Zhou, Xiao Bo ^b   Pi, Dao Ying ^a   Sun, You Xian ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b The Harvard Center for Neurodegeneration & Repair   (United States)

Author keywords

Cancer diagnosis; Constructive approach; Proteomic profiling; Support vector machine ensemble (SVME) design

Indexed keywords

ALGORITHM; ARTICLE; BIOINFORMATICS; CANCER CLASSIFICATION; CANCER DIAGNOSIS; INFORMATION PROCESSING; MASS SPECTROMETRY; OVARY CANCER; PROTEOMICS; VALIDATION STUDY; VECTOR CONTROL;

EID: 33744992230     PISSN: 16720229     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1672-0229(05)03033-0     Document Type: Article

References (11)

1. Valentini, G., et al. 2004. Cancer recognition with bagged ensembles of support vector machines. Neurocomputing 56: 461-466.
2. Bertoni, A., et al. 2005. Bio-molecular cancer prediction with random subspace ensembles of support vector machines. Neurocomputing 63: 535-539.
3. Kuncheva, L.I., et al. 2002. An experimental study on diversity for bagging and boosting with linear classifiers. Information Fusion 3: 245-258.
4. Windeatt, T. 2005. Diversity measures for multiple classifier system analysis and design. Information Fusion 6: 21-36.
5. Zhou, Z.H., et al. 2002. Ensembling neural networks: many could be better than all. Arti. Intell. 137: 239-263.
6. Guyon, I., et al. 2002. Gene selection for cancer classification using support vector machines. Mach. Learn. 46: 389-422.
7. Mao, Y., et al. 2005. Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection. J. Biomed. Biotechnol. 2005: 160-171.
8. Mao, Y., et al. 2005. Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm. J. Zhejiang Univ. Sci. B 6: 961-973.
9. Mao, Y., et al. 2006. Accelerated recursive feature elimination based on support vector machine for key variable identification. Chin. J. Chem. Eng. 14: 65-72.
10. Vapnik, V.N. 1999. The Nature of Statistical Learning Theory. Springer, New York, USA.
11. Lilien, R.H., et al. 2003. Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum. J. Comput. Biol. 10: 925-946.