Model selection with PLANN-CR-ARD

Authors:
Corneliu T. C. Arsene;Paulo J. Lisboa;Elia Biganzoli
Affiliations:
Project Director of National University Research Council, Bucharest, Romania and School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, United Kingdom;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, United Kingdom;Istituto di Statistica Medica e Biometria, Universita degli Studi di Milano, Milano, Italy
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

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Abstract

This paper presents a new compensation mechanism to be used with a Partial Logistic Artificial Neural Network for Competing Risks with Automatic Relevance Determination (PLANN-CR-ARD) and tested comprehensibly on a real breast cancer dataset with excellent convergence properties and numerical stability for the non-linear model. The Model Selection is implemented for the PLANNCRARD model, benefiting from a scaling of the prior error term which together with the data error term forms the total error function that is optimized. The PLANN-CR-ARD proves to be an excellent prognostic tool that can be used in regression analysis tasks such as the survival analysis of cancer datasets.