Thinned ECOC decomposition for gene expression based cancer classification

Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008

Volumn 1, Issue , 2008, Pages 216-221

  Hatami, Nima ^a  

^a SHAHED UNIVERSITY   (Iran)

Author keywords

Cancer classification; Diversity; Error Correcting Output Codes (ECOC); Gene expression data; Multiple Classifier Systems (MCS); Support vector machine; Thinning

Indexed keywords

CANCER CLASSIFICATION; DIVERSITY; ERROR CORRECTING OUTPUT CODES (ECOC); GENE EXPRESSION DATA; MULTIPLE CLASSIFIER SYSTEMS (MCS); THINNING;

BIOACTIVITY; BIOINFORMATICS; CLASSIFIERS; COMPUTER AIDED DIAGNOSIS; DESIGN; GENE EXPRESSION; HEURISTIC ALGORITHMS; HEURISTIC METHODS; IMAGE RETRIEVAL; INTELLIGENT SYSTEMS; SUPPORT VECTOR MACHINES; SYSTEMS ANALYSIS;

MATRIX ALGEBRA;

EID: 67449138899     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2008.308     Document Type: Conference Paper

References (28)

1. Ali. K.M and Pazzani. M.J.: On the Link Between Error Correlation and Error Reduction in Decision Tree Ensembles, Technical Report 95-38, ICS-UCI, 1995.
2. Hatami. N. and Ebrahimpour. R. Combining Multiple Classifiers: Diversify with Boosting and Combining by Stacking, IJCSNS International Journal of Computer Science and Network Security, 7(1), 2007, 127-131.
3. Freund. Y. and Schapire. R.E.: A decision-theoretic generalisation of on-line learning and an application to boosting, J. of Computer and System Science, 55(1), 1997, 119-139.
4. Jacobs. R.A, Jordan. M.I, Nowlan. S.E, G.E. Hinton, Adaptive mixture of experts, Neural Comput., 3, 1991, 79-87.
5. T. G. Dietterich and G. Bakiri, Solving multiclass learning problems via error correcting output codes, J. of Artificial Intelligence Research, 2,1995, 263-286.
6. Ghaderi, R., Arranging simple Neural Networks to solve Complex classification problems, Ph.D. Thesis, Center for Vision, speech and signal processing, university of Surry, (2000).
7. Windeatt, T., Ghaderi, R., Binary labelling and decision level fusion. Inform. Fusion, 2, 2001, 103-112.
8. E.L Allwein, R.E Shapire and Y. Singer, "Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers," Journal of Machine Learning Research, 1, 2000, 113-141.
9. S. Lin and D. J. Costello. Error Control Coding, Second Edition. Prentice-Hall, Inc., 2004.
10. W.W. Peterson and J.R. Weldon. Error-Correcting Codes. MIT press, Cambridge, MA, 1972.
11. W. Utschick, W. Weichselberger, Stochastic organization of output codes in multiclass learning problems, Neural Computation 13 (5) (2001) 1065-1102.
12. K. Crammer, Y. Singer, On the learnability and design of output codes for multiclass problems, Machine Learning 47 (2) (2002) 201-233.
13. O. Pujol, P. Radeva, J. Vitria, Discriminant ecoc: A heuristic method for application dependent design of error correcting output codes, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (6) (2006) 1001- 1007.
14. O. Pujol, S. Escalera, P. Radeva, An incremental node embedding technique for error correcting output codes Pattern Recognition 41 (2008) 713 - 725.
15. X. Long, W. L. Cleveland, Y. L. Yao; Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation, Image and Vision Computing 26 (2008) 578-591.
16. N. Hatami, R. Ebrahimpour, R. Ghaderi, ECOC-Based Training of Neural Networks for Face Recognition, 3rd IEEE International conference on Cybernetics and Intelligent Systems, CIS-RAM08, 2008, (to appear).
17. N. Hatami, S. Seyedtabaii, M. Mikaili, "Combining Classifiers for Handwritten Digit Recognition", In Proceeding of the 3rd International Conference on Information & Knowledge Technology (IKT07), 2007.
18. J.S. Marron, M. Todd, Distance weighted discrimination, Technical Report No. 1339, School of Operations Research and Industrial Engineering, Cornell University, July 2002.
19. S.J. Raudy, A.K. Jain, Small sample size effects in statistical pattern recognition: recommendations for practitioners, IEEE Trans. PAMI 13 (3) (1991) 252-264.
20. T. Hastie, R. Tibshirani, Classification by pairwise grouping, The Annals of Stat. 26 (5) (1998) 451-471.
21. G. Giacinto, F. Roli, An approach to automatic design of multiple classifier systems, Pattern Recognition Letters, v. 22, pp. 25-33, 2001.
22. P.Latinne, O. Debeir, and C. Decaestecker, Limiting the number of trees in random forests, Second International Workshop on Multiple Classifier Systems, pp. 178-187, 2001.
23. R.E. Banfield, L.O. Hall, K.W. Bowyer, W.P. Kegelmeyer, Ensemble diversity measures and their application to thinning, Information Fusion 6 (2005) 49- 62.
24. Ross, D.T., Scherf, U., et al. (2000). Systematic variation in gene expression patterns in human cancer cell lines, Nature Genetics, 24(3), 227-234.
25. Scherf, U., Ross, D.T., et al. (2000). A cDNA microarray gene expression database for the molecular pharmacology of cancer, Nature Genetics, 24(3), 236-244.
26. Alizadeh, A.A., et al. (2000). Distinct types of diffuse large Bcell lymphoma identified by gene expression profiling, Nature, 403, 503-511.
27. K. Fukunaga, Introduction to statistical pattern recognition, Academic Press, Boston, 2rd edition, 1990.
28. N. Hatami, S. Seyedtabaii, Error Correcting Output Codes Using Genetic Algorithm-Based Decoding, 4th International Conference on Networked Computing and Advanced Information Management (NCM2008), IEEE CS Proceedings, 2008,(to appear).