Show simple item record

dc.contributor.authorDu Toit, Jan Valentine
dc.date.accessioned2008-11-28T10:55:17Z
dc.date.available2008-11-28T10:55:17Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/10394/128
dc.descriptionThesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006.
dc.description.abstractIn 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.publisherNorth-West University
dc.subjectAkaike Information Criterionen
dc.subjectAICen
dc.subjectAutomated construction algorithmen
dc.subjectBayesian Model Averagingen
dc.subjectCredit scoringen
dc.subjectData miningen
dc.subjectGeneralized Additive Neural Networken
dc.subjectGANNen
dc.subjectGeneralized Additive Modelen
dc.subjectGAMen
dc.subjectInteractive construction algorithmen
dc.subjectModel averagingen
dc.subjectNeural networken
dc.subjectPartial residuaen
dc.subjectPredictive modelingen
dc.subjectSchwarz information criterionen
dc.subjectSBCen
dc.titleAutomated construction of generalized additive neural networks for predictive data miningen
dc.typeThesisen
dc.description.thesistypeDoctoral


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • ETD@PUK [6438]
    This collection contains the original digitized versions of research conducted at the North-West University (Potchefstroom Campus)

Show simple item record