Extracting rules from pruned neural networks for breast cancer diagnosis

Artificial Intelligence in Medicine

Volumn 8, Issue 1, 1996, Pages 37-51

  Setiono, Rudy ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Breast cancer diagnosis; Neural network pruning; Penalty function; Rule extraction

Indexed keywords

ALGORITHMS; KNOWLEDGE BASED SYSTEMS; NEURAL NETWORKS; ONCOLOGY; PATTERN RECOGNITION;

BREAST CANCER DIAGNOSIS; NEURAL NETWORK PRUNING; PENALTY FUNCTION; RULE EXTRACTION;

COMPUTER AIDED DIAGNOSIS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; PRIORITY JOURNAL;

EID: 0030087643     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/0933-3657(95)00019-4     Document Type: Article

References (22)

1. B. Apolloni, G. Avanzini, N. Cesa-Bianci and G. Ronchine, Diagnosis of epilepsy via backpropagation, in: Proc. Int. Joint Conf. on Neural Networks, Vol. II, Washington DC (1990) 517-574.
2. W. Baxt, Use of an artificial neural network for data analysis in clinical decision making, Neural Computation 2 (1990) 480-489.
3. K.P. Bennett and O.L. Mangasarian, Neural network training via linear programming, in: P.M. Pardalos, ed., Advances in Optimization and Parallel Computing (Elsevier, Amsterdam, 1990) 56-67.
4. J. Boone, G. Gross and G. Shaber, Computer aided radiologie diagnosis using neural networks, in: Proc. Int. Joint Conf. on Neural Networks, Vol. II, Washington DC (1990) 98-101.
5. S. Cho and J.A. Reggia, Multiple disorder diagnosis with adaptive competitive neural networks, Artificial Intelligence Med. 5 (1993) 469-487.
6. F.L. Chung and L. Lee, A node pruning algorithm for backpropagation network, Int. J. Neural Systems 3 (1992) 301-314.
7. M. Hagiwara, A simple and effective method for removal of hidden units and weights, Neurocomputing 6 (1994) 207-218.
8. E.D. Karnin, A simple procedure for pruning back-propagation trained neural networks, IEEE Trans. on Neural Networks 1 (1990) 239-242.
9. O.L. Mangasarian, R. Setiono and W.H. Wolberg, Pattern recognition via linear programming: theory and application to medical diagnosis, in: T.F. Coleman and Y. Li, eds., Large-scale Numerical Optimization (SIAM, Philadelphia, 1990) 22-30.
10. M.C. Mozer and P. Smolensky, Skeletonization: a technique for trimming the fat from a network via relevance assessment, in: D.S. Touretzky, ed., Advances in Neural Information Processing Systems I (Morgan Kaufmann, San Mateo, 1989) 107-115.
11. B. Mulsant and E. Servan-Schreiber, A connectionist approach to the diagnosis of dementia, in: Proc. Int. Joint Conf. on Neural Networks, Vol. I, San Diego, CA (1990) 33-38.
12. P.M. Murphy and D.W. Aha, UCI repository of machine learning databases. Machine-readable data repository (University of California, Department of Information and Computer Science, Irvine, CA, 1992).
13. A. van Ooyen and B. Nienhuis, Improving the convergence of the backpropagation algorithm, Neural Networks 5 (1992) 465-471.
14. D.E. Rumelhart and J.L. McClelland, Parallel Distributed Processing (MIT Press, Cambridge, MA, 1986).
15. R. Setiono, A penalty function approach for pruning feedforward neural network, Neural Computation, submitted.
16. R. Setiono and L.C.K. Hui, Use of quasi-Newton method in a feedforward neural network construction algorithm, IEEE Trans. Neural Networks 6 (1995) 273-277.
17. H.H. Thodberg, Improving generalization of neural networks through pruning, Int. J. Neural Systems 1 (1991) 317-326.
18. G.G. Towell and J.W. Shavlik, Extracting refined rules from knowledge-based neural networks, Machine Learning 13 (1993) 71-101.
19. R.L. Watrous, Learning algorithms for connectionist networks: applied gradient methods for nonlinear optimization, in: Proc. of the IEEE First Int. Conf. on Neural Networks. San Diego (IEEE Press, New York, 1987) 619-627.
20. W.H. Wolberg and O.L. Mangasarian, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, Proc. Natl. Acad. Sci. USA 87 (1990) 9193-9196.
21. W.H. Wolberg, M.A. Tanner and W.Y. Loh, Diagnostic schemes for fine needle aspirates of breast masses, Anal. Quantitative Cytol. Histol. 10 (1988) 225-228.
22. Y. Yoon, R. Brobst, P. Bergstresser and L. Peterson, A desktop neural network for dermatology diagnosis, Neural Network Comput. (Summer 1989) 25-43.