Abstract
In this paper, we present a methodology that facilitates attribute selection and dependence modelling utilising the Gamma Test and a Genetic Algorithm (GA). The Gamma Test, a non-linear analysis algorithm, forms the basis of an objective function that is utilised within a GA. The GA is applied to a range of multivariate dataseis, describing such problems as house price prediction and lymphography classification, in order to select a useful subset of features (or mask). Local Linear Regression is used to contrast the accuracy of models constructed using full masks and suggested masks. Results obtained demonstrate how the presented methodology assists the production of superior models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zurada. J. M. Introduction to Artificial Neural Systems. West Publishing Company (ISBN 0-314-93391), 1992
Cleveland W.S. & Devlin S.J. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting. Journal of the American Statistical Association 1988; 83:596–610
Hall, M.A. & Holmes, G. Benchmarking Attribute Selection Techniques For Discrete Class Data Mining. IEEE Transactions On Knowledge And Data Engineering, 2003. 15(3)
Golberg A. Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, 1989
DeJong, K. A. & Sarma J. Generation gaps revisited. Foundations of Genetic Algorithms 2, San Mateo, CA: Morgan Kaufmann, 1993.
Stefansson A., Konvcar N. & Jones A. J. A note on the Gamma Test. Neural Computing Applications 1997; 5:131–133
Evans D. & Jones A. J. A proof of the Gamma test, Proceedings of the Royal Society London. 2002; A, 458:1–41
Evans D., Jones A. J. & Schmidt W. M. Asymptotic moments of near neighbour distance distributions, Proceedings of the Royal Society London 2002; A,458:1–11
Evans, D. “Data-derived estimates of noise for known smooth models using near-neighbour asymptotics.” Ph.D. dissertation, Department of Computer Science, Cardiff University, Wales, UK, 2002
Durrant P.J. “winGamma TM: a non-linear data analysis and modelling tool for the investigation of non-linear and chaotic systems with applied techniques for a flood prediction system.” Ph.D. dissertation, Department of Computer Science, Cardiff University, Wales, UK, 2001
Harrison D. & Rubinfeld D.L. Hedonic prices and the demand for clean air, J. Environ. Economics & Management 1978; 5:81–102
Hettich S. & Bay S. D. The UCI KDD Archive [http://kdd.ics.uci.edu]. Irvine, CA: University of California, Department of Information and Computer Science. 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag London Limited
About this paper
Cite this paper
Jarvis, P.S., Wilson, I.D., Ware, J.A. (2004). A Genetic Algorithm Approach to Attribute Selection Utilising the Gamma Test. In: Bramer, M., Ellis, R., Macintosh, A. (eds) Applications and Innovations in Intelligent Systems XI. SGAI 2003. Springer, London. https://doi.org/10.1007/978-1-4471-0643-2_18
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0643-2_18
Publisher Name: Springer, London
Print ISBN: 978-1-85233-779-7
Online ISBN: 978-1-4471-0643-2
eBook Packages: Springer Book Archive