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...

Full description

Bibliographic Details
Main Author: Du Toit, Jan Valentine
Published: North-West University 2008
Subjects:
AIC
GAM
SBC
Online Access:http://hdl.handle.net/10394/128
id ndltd-NWUBOLOKA1-oai-dspace.nwu.ac.za-10394-128
record_format oai_dc
spelling 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
collection NDLTD
sources NDLTD
topic 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
spellingShingle 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
_version_ 1716664076800622592