Designing neural networks for modeling biological data: a statistical perspective.
In this paper, we propose a strategy for the selection of the hidden layer size in feedforward neural network models. The procedure herein presented is based on comparison of different models in terms of their out of sample predictive ability, for a specified loss function. To overcome the problem of data snooping, we extend the scheme based on the use of the reality check with modifications apt to compare nested models. Some applications of the proposed procedure to simulated and real data sets show that it allows to select parsimonious neural network models with the highest predictive accuracy.
Department of Economics and Statistics - University of Salerno, Via Giovanni Paolo II, 132. 84084 Fisciano (SA), Italy. larocca@unisa.it.
Michele
M
La Rocca
Cira
C
Perna
D003198:Computer Simulation; D018450:Disease Progression; D004784:Environmental Monitoring / Q000295:instrumentation; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010300:Parkinson Disease / Q000503:physiopathology; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000473:pathology
D017430:Prostate-Specific Antigen