Abstract
One of the most important problems that can be met during a process of modeling of a real system is a problem of insufficient data points. This problem is often discussed in the modeling literature, however, so far no satisfactory solution has been proposed. The aim of this article is to present a method for evaluating the importance of model’s inputs which helps to overcome mentioned problem. The proposed method is an enhanced version of the method of local walking models, introduced two years ago. The practical applicability of the proposed method will be demonstrated via the example of evaluating the significance of 150 potential input variables of the prognostic model of an unemployment rate.
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
Aczel, A. D.: Complete Business Statistics, Richard D. Irwin Inc, Sydney (1993)
Back, B. B.: Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analisis, and Genetic Algorithms, Turku Centre for Computer Science Technical Report No 40, (1996)
Demuth, H., Beale, M.: Neural Network Toolbox Userrss Guide, The Math Works Inc., Natick MA USA (2000)
Lee, T.: Independent Component Analysis-Theory and Applications, Kluwer Academic Publishers, Boston (2001)
Lin, Y., Cunningham, G. A.: A New Approach to Fuzzy-Neural System Modeling, IEEE Transaction on Fuzzy Systems, vol. 3, no. 2 (1995)
Liu, H., Motoda, H.: Feature selection for knowledge discovery and data mining, Kluwer Academic Publishers, USA (1998)
Masters, T.: Practical Neural Networks Recipes in C++, Academic Press Inc (1993)
Piegat, A.: Fuzzy Modeling and Control, Physica-Verlag, New York (1999)
Reed, R. D., Marks, II R. J.: Neural Smithing — Supervised Learning in Feedforward Artificial Neural Networks, The MIT Press, Massachusetts (1999)
Rejer, I.: A method of investigating a significance of input variables in non-linear high-dimensional systems, Artificial Intelligence and Security in Computing Systems, Proceedings on 9th International Conference, ACS’2002, pp. 73–80, Kluwer Academic Publisher, London (2003)
Rejer, I.: A method of modeling a multi-dimensional system via artificial intelligence methods on the example of an unemployment in Poland, The Publishing House of the Szczecin University, Szczecin (2003)
Sugeno, M., Yasukawa, T.: A Fuzzy-Logic-Based Approach to Qualitative Modeling, IEEE Transaction on Fuzzy Systems vol. 1, no. 1 (1993)
Sung, A. H.: Ranking importance of input parameters of neural networks, Expert Systems with Applications 15, pp. 405–411 (1998)
Zeliaś A.: Statistic methods, Polish Economic Publisher, Warszawa (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Science+Business Media, LLC
About this paper
Cite this paper
Rejer, I. (2006). Input’s Significance Evaluation in a Multi Input-Variable System. In: Saeed, K., Pejaś, J., Mosdorf, R. (eds) Biometrics, Computer Security Systems and Artificial Intelligence Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36503-9_27
Download citation
DOI: https://doi.org/10.1007/978-0-387-36503-9_27
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-36232-8
Online ISBN: 978-0-387-36503-9
eBook Packages: Computer ScienceComputer Science (R0)