Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2871, Issue , 2003, Pages 521-530

  Park, Chanho ^a   Cho, Sung Bae ^a  

^a Yonsei University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DISEASES; FEATURE EXTRACTION; INTELLIGENT SYSTEMS; ONCOLOGY; ARRAYS; DATA ACQUISITION; DATABASE SYSTEMS; GENES; PATIENT MONITORING; TUMORS;

CANCER CLASSIFICATION; CLASSIFICATION SYSTEM; DNA MICROARRAY TECHNOLOGY; ENSEMBLE CLASSIFIERS; ENSEMBLE METHODS; EXPRESSION LEVELS; FEATURE CLASSIFIERS; FEATURE SELECTION METHODS;

CLASSIFICATION (OF INFORMATION); DNA;

DNA MICROARRAY; FEATURE CLASSIFICATION; FEATURE SELECTION; GENE EXPRESSION;

EID: 0142245479     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39592-8_74     Document Type: Conference Paper

References (12)

1. Golub, T. R., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. In Science, vol. 286, pp. 531–537, 1999.
2. Yang, Y. H., Dudoit, S., Luu, P. and Speed, T. P.: Normalization for cDNA microarray data. In SPIE Bios 2001, San Jose, California, pp. 1–12, 2001.
3. Model, F., et al.: Feature selection for DNA methylation based cancer classification. In Bioinformatics, vol. 17 Suppl. 1, pp. s157–s164, 2001.
4. Cho, S.-B. and Ryu, J.-W.: Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features. In Proc. of the IEEE, vol. 90, no. 11, pp. 1744–1753, 2002.
5. Li, F. and Stormo, G. D.: Selection of optimal DNA oligos for gene expression arrays. In Bioinformatics. vol. 17, no. 11, pp. 1067–1076, 2001.
6. Mitchell, T. M.: Machine Learning, Carnegie Mellon University, 1997.
7. Khan, J., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. In Nature, vol. 7, no. 6, pp. 673–679, 2001.
8. Duda, R. O., Hart, P. E., and Stork, D. G.: Pattern Classification, 2nd Ed. Wiley Interscience, 2001.
9. Furey, T. S., et al.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. In Bioinformatics, vol. 16, no. 10, pp. 906–914, 2000.
10. Kim, H. D. and Cho, S.-B.: Genetic optimization of structure-adaptive self-organizing map for efficient classification. In Proc. of International Conference on Soft Computing, pp. 34–39, 2000.
11. Ben-Dor, A., et al.: Tissue classification with gene expression profiles. In Journal of Computational Biology, vol. 7. pp. 559–584, 2000.
12. Alizadeh, A. A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. In Nature, vol. 403, pp. 503–511, 2000.