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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Stepwise Linear Regression Dimensionality Reduction in Neural Network Modelling

Addison, Dale, McGarry, Kenneth, Wermter, Stefan and MacIntyre, John (2004) Stepwise Linear Regression Dimensionality Reduction in Neural Network Modelling. In: Proceedings of the International Conference on Artificial Intelligence and Applications, 16-18 February 2004, Innsbruck, Austria.

Item Type: Conference or Workshop Item (Paper)


This work considers the applicability of applying the derivatives of stepwise linear regression modelling (specifically the p-values which indicate the importance of a variable to the modelling process) as a feature extraction technique. We utilise it in conjunction with several data sets of varying levels of complexity, and compare our results to other dimensionality reduction techniques such as genetic algorithms, sensitivity analysis and linear principal components analysis prior to data modelling using several different neural network models. Our results indicate that stepwise linear regression is highly effective in this role with results comparable to and sometimes superior then more established techniques

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Depositing User: Kenneth McGarry


Item ID: 5281
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

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Date Deposited: 11 Mar 2015 11:44
Last Modified: 18 Dec 2019 15:37


Author: Kenneth McGarry ORCID iD
Author: Dale Addison
Author: Stefan Wermter
Author: John MacIntyre

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence
Computing > Information Systems

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