Bio-molecular cancer prediction with random subspace ensembles of support vector machines

Neurocomputing

Volumn 63, Issue SPEC. ISS., 2005, Pages 535-539

  Bertoni, Alberto ^a   Folgieri, Raffaella ^a   Valentini, Giorgio ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

DNA microarray; Ensemble of learning machines; Molecular classification of tumors; Random subspace; Support vector machines

Indexed keywords

ARRAYS; DISEASES; GENES; MACHINE COMPONENTS; SET THEORY; VECTORS;

BIO-MOLECULAR DIAGNOSIS; FEATURE SELECTION METHODS; GENE EXPRESSION DATA; SUPPORT VECTOR MACHINES (SVM);

LEARNING SYSTEMS;

ARTICLE; CANCER DIAGNOSIS; CANCER GENETICS; CLASSIFICATION; COLON ADENOCARCINOMA; DIAGNOSTIC APPROACH ROUTE; DNA MICROARRAY; GENE EXPRESSION; HUMAN; HUMAN TISSUE; LEARNING; MEDULLOBLASTOMA; PREDICTION; PRIORITY JOURNAL; TRAINING;

EID: 12144262779     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.07.007     Document Type: Article

References (12)

1. A. Alizadeh et al. Towards a novel classification of human malignancies based on gene expression J. Pathol. 195 2001 41-52
2. U. Alon et al. Broad patterns of gene expressions revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays Proc. Natl. Acad. Sci. 96 1999 6745-6750
3. E. Amaldi V. Kann On the approximation of minimizing non zero variables or unsatisfied relations in linear systems Theor. Comput. Sci. 209 1998 237-260
4. R. Bellman Adaptive Control Processes a Guided Tour 1961 Princeton University Press Princeton, NJ
5. S. Dudoit J. Fridlyand T. Speed Comparison of discrimination methods for the classification of tumors using gene expression data J. Am. Stat. Assoc. 97 457 2002 77-87
6. T. Furey N. Cristianini N. Duffy D. Bednarski M. Schummer D. Haussler Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics 16 10 2000 906-914
7. I. Guyon J. Weston S. Barnhill V. Vapnik Gene selection for cancer classification using support vector machines Mach. Learning 46 1/3 2002 389-422
8. T. Ho The random subspace method for constructing decision forests IEEE Trans. Pattern Anal. Mach. Intel. 20 8 1998 832-844
9. S. Pomeroy et al. Gene expression-based classification and outcome prediction of central nervous system embryonal tumors Nature 415 2002 436-442
10. M. Skurichina R. Duin Bagging, boosting and the random subspace method for linear classifiers Pattern Anal. Appl. 5 2 2002 121-135
11. G. Valentini F. Masulli NEURObjects an object-oriented library for neural network development Neurocomputing 48 1-4 2002 623-646
12. G. Valentini M. Muselli F. Ruffino Cancer recognition with bagged ensembles of support vector machines Neurocomputing 56 2004 461-466