Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit
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 i...
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ndltd-NWUBOLOKA1-oai-dspace.nwu.ac.za-10394-1282014-04-16T03:54:50ZAutomated construction of generalized additive neural networks for predictive data mining / Jan Valentine du ToitDu Toit, Jan ValentineAkaike Information CriterionAICAutomated construction algorithmBayesian Model AveragingCredit scoringData miningGeneralized Additive Neural NetworkGANNGeneralized Additive ModelGAMInteractive construction algorithmModel averagingNeural networkPartial residuaPredictive modelingSchwarz information criterionSBCIn 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.Thesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006.North-West University2008-11-28T10:55:17Z2008-11-28T10:55:17Z2006Thesishttp://hdl.handle.net/10394/128 |
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Akaike Information Criterion AIC Automated construction algorithm Bayesian Model Averaging Credit scoring Data mining Generalized Additive Neural Network GANN Generalized Additive Model GAM Interactive construction algorithm Model averaging Neural network Partial residua Predictive modeling Schwarz information criterion SBC |
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Akaike Information Criterion AIC Automated construction algorithm Bayesian Model Averaging Credit scoring Data mining Generalized Additive Neural Network GANN Generalized Additive Model GAM Interactive construction algorithm Model averaging Neural network Partial residua Predictive modeling Schwarz information criterion SBC Du Toit, Jan Valentine Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit |
description |
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. === Thesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006. |
author |
Du Toit, Jan Valentine |
author_facet |
Du Toit, Jan Valentine |
author_sort |
Du Toit, Jan Valentine |
title |
Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit |
title_short |
Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit |
title_full |
Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit |
title_fullStr |
Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit |
title_full_unstemmed |
Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit |
title_sort |
automated construction of generalized additive neural networks for predictive data mining / jan valentine du toit |
publisher |
North-West University |
publishDate |
2008 |
url |
http://hdl.handle.net/10394/128 |
work_keys_str_mv |
AT dutoitjanvalentine automatedconstructionofgeneralizedadditiveneuralnetworksforpredictivedataminingjanvalentinedutoit |
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1716664076800622592 |