Input selection for support vector machines using genetic algorithms

Authors:
Hee-Jun Song;Seon-Gu Lee;Sung-Hoe Huh
Affiliations:
Department of Electrical Engineering, Korea University, Seoul, Korea;Department of Electrical Engineering, Korea University, Seoul, Korea;Dept. of Environmental Science & Engineering, Ewha Woman’s Univ., Seoul, Korea
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 6
Cited 0

Successive identification of a fuzzy model and its applications to prediction of a complex system

Fuzzy Sets and Systems
The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
A new approach to fuzzy-neural system modeling

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, an effective and simple method of input selection for nonlinear regression modeling using Support Vector Machine combined with Genetic Algorithm is proposed. Genetic Algorithm is used in order to extract dominant inputs from a large number of potential inputs in input selection process. Support Vector Machine is used as a nonlinear regressor with the selected dominant inputs. The proposed method is applied to the Box-Jenkins furnace benchmark to verify its effectiveness.