Model selection with PLANN-CR-ARD. (English)
Cabestany, Joan (ed.) et al., Advances in computational intelligence. 11th international work-conference on artificial neural networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8‒10, 2011. Proceedings, Part II. Berlin: Springer (ISBN 978-3-642-21497-4/pbk). Lecture Notes in Computer Science 6692, 210-219 (2011).
Summary: 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 PLANN-CR-ARD 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.
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