A model for single and multiple knowledge based networks

Artificial Intelligence in Medicine

Volumn 28, Issue 2, 2003, Pages 141-163

  Bologna, G ^a  

^a SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

Decision trees; Electrophoresis; Ensembles; Neural networks; Rule insertion extraction

Indexed keywords

DIAGNOSIS; DISEASES; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; POLYNOMIALS;

RULE EXTRACTION;

KNOWLEDGE BASED SYSTEMS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLINICAL PRACTICE; DECISION SUPPORT SYSTEM; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; ELECTROPHORESIS; INFORMATION PROCESSING; LYMPHATIC LEUKEMIA; LYMPHOMA; MODEL; NONHODGKIN LYMPHOMA; PRIORITY JOURNAL; PROGNOSIS;

EID: 0042168792     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0933-3657(03)00055-1     Document Type: Article

References (33)

1. Andrews R, Geva S. Rule extraction from a constrained error back-propagation multi-layer perceptron. In: Proceedings of the Australian Conference on Neural Networks; 1994. p. 9-12.
2. Andrews R., Diederich J., Tickle A.B., Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Syst. 8(6):1995;373-389.
3. Appel R.D., Palagi P., Vargas J.R., Hochstrasser D.F., Melanie II: a third generation software package for analysis of two-dimensional electrophoresis images. Electrophoresis. 18:1997;2735-2748.
4. Bauer E., Kohavi R., An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learning. 36:1999;105-139.
5. Baxt W., Improving the accuracy of an artificial neural network using multiple differently trained networks. Neural Comput. 4:1992;772-780.
6. Blake CL, Merz CJ. UCI repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository.html . Irvine (CA): Department of Information and Computer Science, University of California.
7. Bologna G, Pellegrini C. Rule extraction from modular transparent boxes. In: Proceedings of NEURAP; 1998. p. 393-8.
8. Bologna G, Pellegrini C. Rule extraction from the oblique multi-layer perceptron. In: Proceedings of the Australian Conference on Neural Networks; 1998. p. 260-4.
9. Bologna G. Symbolic rule extraction from the DIMLP neural network. In: Wermter S, Sun R, editors. Neural hybrid systems. Berlin: Springer; 2000. p. 240-54.
10. Breiman L. Bagging predictors. Mach Learning 1996;26:123-40.
11. Breiman L. Bias, variance, and arcing classifiers. California: Technical Report, Statistics Department, University of California, available at ftp://ftp.stat.berkeley.edu/pub/users/breiman/ ; 1996. p. 460.
12. Clark P., Niblett T., The CN2 induction algorithm. Mach Learning. 3(4):1989;261-283.
13. Craven M, Shavlik J. Using sampling and queries to extract rules from trained neural networks. In: Proceedings of the International Conference on Machine Learning; 1994. p. 37-45.
14. Craven M, Shavlik J. Extracting tree-structured representations of trained networks. In: Proceedings of Neural Information Processing Systems, vol. 8. 1996. p. 24-30.
15. Craven M. Extracting comprehensible models from trained neural networks. Wisconsin: PhD Thesis, University of Wisconsin; 1996.
16. Duch W., Adamczak R., Grabczewski K., A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. Trans Neural Netw. 12(2):2001;277-306.
17. Fahlman SE. An empirical study of learning speed in back-propagation networks. Melon: Technical Report CMU-CS-88-162, Carnegie Melon University; 1988.
18. Hayward R, Ho-Stuart C, Diederich J, Pop E. Ruleneg: extracting rules from a trained ANN by stepwise negation. Australia: Technical Report, Queensland University of Technology; 1996.
19. Jang J.S.R., ANFIS. Adaptive-network-based fuzzy inference system. Trans Syst Man Cybernetics. 23:1993;665-685.
20. Kasabov N, Woodford BJ. Rule insertion and rule extraction from evolving fuzzy networks: algorithms and applications for building adaptive intelligent expert systems. In: Proceedings of the International Fuzzy Systems Conference; 1999. p. 1406-11.
21. Krishnan R, Sivakumar G, Bhattacharya P. A search technique for rule extraction from trained neural networks. Pattern Recognition Lett 1999;20:273-80.
22. Maire F., A partial order for the M-of-N rule-extraction algorithm. Trans Neural Netw. 8(6):1997;1542-1544.
23. Nauck D., Kruse R., Obtaining interpretable fuzzy classification rules from medical data. Artif Intell Med. 16:1999;149-169.
24. Omlin C.W., Giles C.L., Extraction of rules from discrete-time recurrent neural networks. Neural Netw. 9(1):1996;41-52.
25. Opitz D., Maclin R., Popular ensemble methods: an empirical study. Artif Intell Res. 11:1999;169-198.
26. Quinlan JR. C4.5: programs for machine learning. Los Altos: Kaufmann (Morgan) (1993).
27. Saito K, Nakano R. Medical diagnostic system based on PDP model. In: Proceedings of the International Conference on Neural Networks; 1988. p. 255-62.
28. Setiono R., Huan L., NeuroLinear: from neural networks to oblique decision rules. Neurocomputing. 17:1997;1-24.
29. Setiono R., Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med. 18:2000;205-219.
30. Taha I.A., Ghosh J., Symbolic interpretation of artificial neural networks. Trans Neural Netw. 11(3):1999;448-463.
31. Towell G., Shavlik J., The extraction of refined rules from knowledge based neural networks. Mach Learning. 131:1993;71-101.
32. Werbos P. Beyond regression: new tools for prediction and analysis in the behavioral sciences. Harvard: PhD Thesis, University of Harvard; 1974.
33. Zadeh L.A., Fuzzy sets. Inf Control. 8:1965;338-353.