Automation of Generalized Additive Neural Networks for Predictive Data Mining

For a new technology to make the step from experimental technology to mainstream technology, tools need to be created to facilitate the use of the developed technology in the envisaged application area. Generalized additive neural networks provide an attractive framework that shows promise in the field of predictive data mining. However, the construction of such networks is very time consuming and subjective, because it depends on the user to interpret partial residual plots and to make changes in the neural network architecture. For this technology to be accepted as a serious modeling option in the field of predictive data mining the construction process needs to be automated and the benefits of using the technique must be clearly illuminated. This article shows how intelligent search may be used to replace subjective human judgment with objective criteria and make generalized additive neural networks an attractive option for the modeler.

[1]  George Luger,et al.  Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition) , 2004 .

[2]  Daryl Pregibon,et al.  Estimating Optimal Transformations for Multiple Regression and Correlation: Comment , 1985 .

[3]  M. Ezekiel.  A Method of Handling Curvilinear Correlation for Any Number of Variables , 1924 .

[4]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[5]  Samuel D. Conte,et al.  Elementary Numerical Analysis: An Algorithmic Approach , 1975 .

[6]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[7]  R. Tibshirani,et al.  Generalized additive models for medical research , 1986, Statistical methods in medical research.

[8]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[9]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[10]  Herbert K. H. Lee,et al.  Model selection and model averaging for neural networks , 1998 .

[11]  W. A. Larsen,et al.  The Use of Partial Residual Plots in Regression Analysis , 1972 .

[12]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[13]  William J. E. Potts,et al.  Generalized additive neural networks , 1999, KDD '99.

[14]  Susanna Elisabeth Sophia Campher.  Comparing generalised additive neural networks with decision trees and alternating conditional expectations , 2008 .

[15]  George F. Corliss.  Which Root Does the Bisection Algorithm Find , 1977 .

[16]  D. Rubinfeld,et al.  Hedonic housing prices and the demand for clean air , 1978 .

[17]  J. Friedman,et al.  Estimating Optimal Transformations for Multiple Regression and Correlation. , 1985 .

[18]  J. Friedman.  A VARIABLE SPAN SMOOTHER , 1984 .

[19]  David E. Booth,et al.  Seeing a curve in multiple regression , 1995 .

[20]  Douglas M. Hawkins,et al.  Discussion of ‘Flexible Parsimonious Smoothing and Additive Modeling’ , 1989 .

[21]  E. B. Fowlkes,et al.  Estimating Optimal Transformations for Multiple Regression and Correlation: Comment , 1985 .

[22]  Consuelo Pizarro,et al.  GA-ACE : Alternating conditional expectations regression with selection of significant predictors by genetic algorithms , 2006 .

[23]  Edwin P. D. Pednault,et al.  Transform Regression and the Kolmogorov Superposition Theorem , 2006, SDM.

[24]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[25]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[26]  D. Toit,et al.  Automated construction of generalized additive neural networks for predictive data mining , 2006 .

[27]  P. Deuflhard.  Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms , 2011 .