High-dimensional micro-array data classification using minimum description length and domain expert knowledge

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

Volumn 4031 LNAI, Issue , 2006, Pages 790-799

  Bosin, Andrea ^a   Dessì, Nicoletta ^a   Pes, Barbara ^a  

^a UNIVERSITY OF CAGLIARI   (Italy)

Author keywords

Bio informatics; Data Mining Knowledge Discovery; Machine Learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; FEATURE EXTRACTION; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS;

ADAPTIVE BAYESIAN NETWORK (ABN); BINARY CLASSIFICATIONS; BIOINFORMATICS; DOMAIN EXPERT KNOWLEDGE; MINIMUM DESCRIPTION LENGTH; MULTI-TARGET CLASSIFICATION; SUPPORT VECTOR MACHINES (SVM);

DATA REDUCTION;

EID: 33746210686     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11779568_85     Document Type: Conference Paper

References (21)

1. Hardimann G., Microarray methods and applications: Nuts & bolts. DNA Press, 2003.
2. Golub T.R., et al., Molecular classification of cancer. Class discovery and class prediction by gene expression monitoring. Science, 286, 531-537, 1999.
3. Khan J., et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 7, 673-679, 2001.
4. Guyon I., Weston J., Barnhill S., and Vapnik V., Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning, 46 (1-3): 389-422, 2002.
5. Bosin A., Dessl N., Liberati D., Pes B., Learning Bayesian Classifiers from Gene-Expression MicroArray Data, CIBB-WILF 2005.
6. Furey T., Cristianini N. et al, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16:906-914, 2002.
7. Valentini G., Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles, Artificial Intelligence in Medicine, 2002.
8. Barron A., Rissanen J., Yu B., The minimum description length principle in coding and modelling, IEEE Transactions on Information Theory, 44: 2743-2760, 1998.
9. St Jude Children's Research Hospital. http://www.stjuderesearch.org/data/ ALL1/.
10. Blum A., Langley P., Selection of relevant features and examples in machine learning, Artificial Intelligence, 97:245-271, 1997.
11. Kohavi R., John G., Wrappers for feature subset selection. Artificial Intelligence, 97:273-324, 1997.
12. Kononenko I., On biases in estimatine multi-valued attributes, IJCAI95, 1034-1040, 1995.
13. Yarmus J.S., ABN: A Fast, Greedy Bayesian Network Classifier, 2003. http://om.oracle.com/products/bi/pdf/adaptive_bayes-net.pdf.
14. Friedman N., Geiger D., Goldszmidt M., Bayesian Network Classifiers, Machine Learning, 29:131-161, 1997.
15. Keogh E., Pazzani M.J., Learning the structure of augmented Bayesian classifiers, International Journal on Artificial Intelligence Tools, Vol. 11, No. 4, 587-601, 2002.
16. Cheng G., Greiner R., Comparing Bayesian Network Classifiers, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, Inc., San Francisco, 1999.
17. Vapnik V., Statistical Learning Theory, Wiley-Interscience, New York, NY, USA, 1998.
18. Yeoh E.J. et al., Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling, Cancer Cell, 1:133-143, 2002.
19. Mukherjee S., Classifying Microarray Data Using Support Vector Machines, Understanding And Using Microarray Analysis Techniques: A Practical Guide. Kluwer Academic Publishers, Boston, MA, 2003.
20. Liu H., Li J., Wong L., A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns, Genome informatics 13:51-60, 2002.
21. http://www.oracle.com