Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit

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dc.contributor.author Du Toit, Jan Valentine
dc.date.accessioned 2008-11-28T10:55:17Z
dc.date.available 2008-11-28T10:55:17Z
dc.date.issued 2006
dc.identifier.uri http://hdl.handle.net/10394/128
dc.description Thesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006.
dc.description.abstract In this thesis Generalized Additive Neural Networks (GANNs) are studied in the context of predictive Data Mining. A GANN is a novel neural network implementation of a Generalized Additive Model. Originally GANNs were constructed interactively by considering partial residual plots. This methodology involves subjective human judgment, is time consuming, and can result in suboptimal results. The newly developed automated construction algorithm solves these difficulties by performing model selection based on an objective model selection criterion. Partial residual plots are only utilized after the best model is found to gain insight into the relationships between inputs and the target. Models are organized in a search tree with a greedy search procedure that identifies good models in a relatively short time. The automated construction algorithm, implemented in the powerful SAS® language, is nontrivial, effective, and comparable to other model selection methodologies found in the literature. This implementation, which is called AutoGANN, has a simple, intuitive, and user-friendly interface. The AutoGANN system is further extended with an approximation to Bayesian Model Averaging. This technique accounts for uncertainty about the variables that must be included in the model and uncertainty about the model structure. Model averaging utilizes in-sample model selection criteria and creates a combined model with better predictive ability than using any single model. In the field of Credit Scoring, the standard theory of scorecard building is not tampered with, but a pre-processing step is introduced to arrive at a more accurate scorecard that discriminates better between good and bad applicants. The pre-processing step exploits GANN models to achieve significant reductions in marginal and cumulative bad rates. The time it takes to develop a scorecard may be reduced by utilizing the automated construction algorithm.
dc.publisher North-West University
dc.subject Akaike Information Criterion en
dc.subject AIC en
dc.subject Automated construction algorithm en
dc.subject Bayesian Model Averaging en
dc.subject Credit scoring en
dc.subject Data mining en
dc.subject Generalized Additive Neural Network en
dc.subject GANN en
dc.subject Generalized Additive Model en
dc.subject GAM en
dc.subject Interactive construction algorithm en
dc.subject Model averaging en
dc.subject Neural network en
dc.subject Partial residua en
dc.subject Predictive modeling en
dc.subject Schwarz information criterion en
dc.subject SBC en
dc.title Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit en
dc.type Thesis en
dc.description.thesistype Doctoral

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    This collection contains the original digitized versions of research conducted at the North-West University (Potchefstroom Campus)

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