Classification of medical data with a robust multi-level combination scheme

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 3, Issue , 2004, Pages 2483-2487

  Tsirogiannis, G L ^a   Frossyniotis, D ^a   Stoitsis, J ^a   Golemati, S ^a   Stafylopatis, A ^a   Nikita, K S ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; INTELLIGENT CLASSIFIERS; MULTI-LAYERED FEED-FORWARD NEURAL NETWORKS; SUPPORT VECTOR MACHINES (SVM);

ALGORITHMS; DECISION THEORY; ERROR ANALYSIS; FEEDFORWARD NEURAL NETWORKS; MULTILAYER NEURAL NETWORKS; PROBABILITY; RELIABILITY;

COMPUTER AIDED DIAGNOSIS;

EID: 10844259824     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2004.1381020     Document Type: Conference Paper

References (22)

1. Alpaydin, E. Multiple networks for function learning. I, 27-32. 1993. IEEE International Conference on Neural Networks.
2. Alpaydin, E. Techniques for combining multiple learners. 2,6-12. 1998. ICSC Press. Proceedings of Engineering of Intelligent Systems.
3. Bauer, E. and Kohavi, R., "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting and Variants," Machine Learning, vol. 36 pp. 105-139, 1999.
4. Breiman, L. Bagging Predictors. 421. 1994. Department of Statistics, University of California, Berkeley.
5. Breiman, L., Friedman, J., Olshen, R., and Stone, C., Classification and Regression Trees New York: Chapman and Hall, 1994.
6. Drucker, H., Boosting using Neural Networks Springer-Verlag, 1998.
7. Drucker, H., ortes, C., ackel, L., eCun, Y., and apnik, V. Boosting and other machine learning algorithms. 53-61. 1994. New Brunswick, New Jersey. Proceedings on the Eleventh International Conference on Machine Learning.
8. Freund, Y., "Boosting a weak learning algorithm by majority," Information and computation, vol. 2 pp. 256-285, 1996.
9. Freund, Y. and Schapire, R. Experiments with a new boosting algorithm. 148-156. 1996. Proceedings on the Thirteenth International Conference on Machine Learning.
10. Frosyniotis, D., Stafylopatis, A., and Likas, A., "A divide-and-conquer method for multi-net classifiers," Pattern Analysis and Applications, vol. 6 pp. 32-40, 2003.
11. Hansen, L. and Salamon, P., "Neural Network Ensembles," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12 pp. 993-1001, 1990.
12. Maclin, R. and Opitz, D., "Popular Ensemble Methods: An empirical study," Journal of Artificial Intelligence Research, vol. 11 pp. 169-198, 1999.
13. Mougiakakou, S. G., Golemati, S., Gousias, I., Nikita, K. S., and Nicolaides, A. Computer-Aided Diagnosis of Carotid Atherosclerosis using Laws Texture Features and a Hybrid Trained Neural Network. 2003. 25th Annual International Conference of the IEEE Engineering In Medicine and Biology Society.
14. Platt, J., Fast Training of Support Vector Machines using Sequential Minimal Optimization. Advances in Kernel Methods - Support Vector Learning MIT Press, 1998.
15. Quinlan, J. R. Bagging, Boosting and C4.5. 725-730. 1996. AAAI Press and the MIT Press. Proceedings on the Thirteenth National Conference on Artificial Intelligence.
16. Quinlan, J. R., C4.5: Programs for Machine Learning San Matco: Morgan Kaufmann, 1993.
17. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., "Learning representations of backpropagation errors," Nature, vol. 323 pp. 533-536, 1986.
18. Sharkey, A. J. C., Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems Springer Press, 1999.
19. UCI Machine Learning Databases Repository. University of California-Irvine, Department of Information and Computer Science, [ftp://ftp.ics.edu/pub/machine-learning-databases]
20. rd Hellenic Conference on Artificial Intelligence
21. Vapnik, V. N., "Principles of risk minimization for learning theory," Advances in Neural Information Processing Systems, vol. 4 pp. 831-838, 1992.
22. Vapnik, V. N., The Nature of Statistical Learning Theory New York: Wiley, 1998.