Selection of artificial neural network models for survival analysis with Genetic Algorithms

Computational Statistics and Data Analysis

Volumn 52, Issue 1, 2007, Pages 30-42

  Ambrogi, Federico ^a   Lama, Nicola ^a   Boracchi, Patrizia ^a   Biganzoli, Elia ^b  

^a UNIVERSITY OF MILAN   (Italy)

^b NATIONAL CANCER INSTITUTE   (Italy)

Author keywords

Competing risks; Genetic Algorithms; Neural networks; Regularization

Indexed keywords

BIOMEDICAL ENGINEERING; GENETIC ALGORITHMS; MULTILAYER NEURAL NETWORKS; RISK ANALYSIS; STATISTICAL METHODS;

COMPETING RISKS; INFORMATION CRITERION; REGULARIZATION;

MATHEMATICAL MODELS;

EID: 34548266303     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2007.05.001     Document Type: Article

References (38)

1. Akaike, H., 1973. Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csaki, F. (Eds.), Second International Symposium on Information Theory.
2. Berger J.O. Statistical Decision Theory and Bayesian Analysis (1985), Springer, New York
3. Biganzoli E., Boracchi P., Mariani L., and Marubini E. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Statist. Med. 17 10 (1998) 1169-1186
4. Biganzoli E., Boracchi P., and Marubini E. A general framework for neural network models on censored survival data. Neural Networks 15 2 (2002) 209-218
5. Biganzoli E., Boracchi P., Coradini D., Daidone M.G., and Marubini E. Prognosis in node-negative primary breast cancer: a neural network analysis of risk profiles using routinely assessed factors. Ann. Oncol. 14 (2003) 1484-1493
6. Biganzoli E., Boracchi P., Ambrogi F., and Marubini E. Artificial neural network models for the joint modeling of discrete cause specific hazards. Artificial Intelligence Med. 37 (2006) 119-130
7. Bishop C.M. Neural Networks for Pattern Recognition (1995), Clarendon Press, Oxford
8. Bozdogan H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52 3 (1987) 345-370
9. Bozdogan H. Akaike's information criterion and recent developments in information complexity. J. Math. Psych. 44 (2000) 62-91
10. Brent R. Algorithms for Minimization without Derivatives (1973), Prentice-Hall, Englewood Cliffs, NJ
11. Chatterjee S., Laudato M., and Lucy A.L. Genetic algorithms and their statistical applications: an introduction. Comput. Statist. Data Anal. 22 (1996) 633-651
12. Davis L. Handbook of Genetic Algorithms (1991), Van Nostrand Reinhold, New York, NY
13. De Jong, K.A., Spears, W., 1990. An analysis of the interacting roles of population size and crossover in genetic algorithms. Proceedings of the First International Conference on Parallel Problem Solving from Nature. Morgan Kaufman, Los Altos, CA.
14. Efron B. Logistic regression, survival analysis and the Kaplan-Meyer curve. J. Amer. Statist. Assoc. 83 (1988) 414-425
15. Geisser S. The Predictive Sample Reuse Method with Applications. J. Amer. Statist. Assoc. 50 (1975) 320-328
16. Geman S., Bienenstock E., and Doursat D. Neural networks and the bias/variance dilemma. Neural Comput. 4 (1992) 1-58
17. Gray R.J. Hazard rate regression using ordinary nonparametric regression smoothers. J. Comput. Graph. Statist. 5 (1996) 190-207
18. Harrell, Jr., F.E., and with contributions from many other users, 2006. Hmisc: Harrell Miscellaneous. R package version 3.1-2. 〈http://biostat.mc.vanderbilt.edu/s/Hmisc〉, 〈http://biostat.mc.vanderbilt.edu/twiki/pub/Main/RS/sintro.pdf〉, 〈http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatReport/summary.pdf〉.
19. Holland J.H. Adaptation in Natural and Artificial Systems (1975), University of Michigan Press, MI, USA
20. Linhart H., and Zucchini W. Model Selection (1986), Wiley, New York
21. Lisboa P.J., Wong H., Harris P., and Swindell R. A Bayesian neural network approach for modeling censored data with an application to prognosis after surgery for breast cancer. Artificial Intelligence Med. 28 1 (2003) 1-25
22. Marubini E., and Valsecchi M.G. Analysing Survival Data from Clinical Trials and Observational Studies (1995), Wiley, Chichester
23. McCullagh, P., Nelder, J.A., 1989. Generalized Linear Models. Chapman & Hall, London, pp. 23-25.
24. Mitchell M. An Introduction to Genetic Algorithms (1996), MIT Press, Cambridge
25. Moody J. Prediction risk and architecture selection for neural networks. In: Cherkassky V., et al. (Ed). From Statistics to Neural Networks, NATO ASI Series F (1994), Springer, Berlin
26. Moody, J.E., 1992. The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems. In: Moody, J.E., Hanson, S.J., Lippmann, R.P. (Eds.), Advances in Neural Information Processing Systems, vol. 4, Morgan Kaufmann, San Mateo, CA, pp. 847-854.
27. Murata N., Yoshizawa S., and Amari S. Network information criterion--determining the number of hidden units for artificial neural network models. IEEE Trans. Neural Networks 5 (1994) 865-872
28. Nelder J.A., and Mead R. A simplex algorithm for function minimization. Comput. J. 7 (1965) 308-313
29. R Development Core Team, 2006. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL 〈http://www.R-project.org〉.
30. Ripley B.D. Pattern Recognition and Neural Networks (1996), Cambridge University Press, Cambridge
31. Schaffer J.D., Caruana R.A., Eshelman L.J., and Das R. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Schaffer J.D. (Ed). Proceedings of the Third International Conference on Genetic Algorithms (1989), Morgan Kaufmann, Los Altos, CA
32. Shapiro, J., Prügel-Bennett, A., Rattray, M., 1994. A statistical mechanical formulation of the dynamics of genetic algorithms. In: Lecture Notes in Computer Science, vol. 865, pp. 17-27.
33. Stone M. Cross-validatory choice and assessment of statistical predictions. J. Roy. Statist. Soc. B 36 (1974) 111-147
34. Syswerda, G., 1989. Uniform crossover in genetic algorithms. In: Schaffer, H. (Ed.), Third International Conference on Genetic Algorithms, vols. 2-9, Morgan Kaufmann, San Mateo.
35. Urmanov A.M., Gribok A.V., Hines J.W., and Uhrig R.E. An information approach to regularization parameter selection under model misspecification. Inverse Problems 18 (2002) 1207-1228
36. Vapnik V.N. The Nature of Statistical Learning (1998), Springer, New York
37. Venables W.N., and Ripley B.D. Modern Applied Statistics with S. fourth ed (2002), Springer, Berlin
38. Wahba G. Three topics in ill posed inverse problems. In: Engl M., and Groetsch G. (Eds). Inverse and Ill-Posed Problems (1987), Academic Press, New York