Evolutionary approach to overcome initialization parameters in classification problems

Authors:
P. Isasi;F. Fernandez
Affiliations:
Computer Science Department, Universidad Carlos III de Madrid;Computer Science Department, Universidad Carlos III de Madrid
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

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Abstract

It is a widely accepted fact that no single Machine Learning System (MLS) gets the smaller classification error on all data sets. Different algorithms fit better to certain problems and it is interesting to combine them in some way to improve the overall accuracy. In this paper, we propose a method to construct a new MLS from given ones. It is based on the selection of the system that will perform better on a particular data set. We study several ways of selecting the systems and carry out experiments with well-known MLS on the Holte data set.