Choosing among algorithms to improve accuracy View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2003-06-18

AUTHORS

José Ramón Quevedo , Elías F. Combarro , Antonio Bahamonde

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. More... »

PAGES

246-253

References to SciGraph publications

Book

TITLE

Computational Methods in Neural Modeling

ISBN

978-3-540-40210-7
978-3-540-44868-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-44868-3_32

DOI

http://dx.doi.org/10.1007/3-540-44868-3_32

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007647528


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