International Journal of Computational Intelligence Systems

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Volume 9, Issue 2, April 2016, Pages 263 - 280

An algorithm evaluation for discovering classification rules with gene expression programming

Authors
Alain Guerrero-Enamorado1, alaing@uci.cu, Carlos Morell2, cmorellp@uclv.edu.cu, Amin Y. Noaman3, anoaman@kau.edu.sa, Sebastián Ventura4, sventura@uco.es
1Universidad de las Ciencias Informáaticas (UCI), Habana, Cuba
2Universidad Central de Las Villas (UCLV), Villa Clara, Cuba
3King Abdulaziz University, Jeddah, Saudi Arabia
4University of Córdoba (UCO), Córdoba, Spain, King Abdulaziz University, Jeddah, Saudi Arabia
Received 17 April 2015, Accepted 27 December 2015, Available Online 1 April 2016.
DOI
10.1080/18756891.2016.1150000How to use a DOI?
Keywords
Genetic programming; Gene expression programming; Classification rules; Discriminant functions
Abstract

In recent years, evolutionary algorithms have been used for classification tasks. However, only a limited number of comparisons exist between classification genetic rule-based systems and gene expression programming rule-based systems. In this paper, a new algorithm for classification using gene expression programming is proposed to accomplish this task, which was compared with several classical state-of-the-art rule-based classifiers. The proposed classifier uses a Michigan approach; the evolutionary process with elitism is guided by a token competition that improves the exploration of fitness surface. Individuals that cover instances, covered previously by others individuals, are penalized. The fitness function is constructed by the multiplying three factors: sensibility, specificity and simplicity. The classifier was constructed as a decision list, sorted by the positive predictive value. The most numerous class was used as the default class. Until now, only numerical attributes are allowed and a mono objective algorithm that combines the three fitness factors is implemented. Experiments with twenty benchmark data sets have shown that our approach is significantly better in validation accuracy than some genetic rule-based state-of-the-art algorithms (i.e., SLAVE, HIDER, Tan, Falco, Bojarczuk and CORE) and not significantly worse than other better algorithms (i.e., GASSIST, LOGIT-BOOST and UCS).

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 2
Pages
263 - 280
Publication Date
2016/04/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1150000How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Alain Guerrero-Enamorado
AU  - Carlos Morell
AU  - Amin Y. Noaman
AU  - Sebastián Ventura
PY  - 2016
DA  - 2016/04/01
TI  - An algorithm evaluation for discovering classification rules with gene expression programming
JO  - International Journal of Computational Intelligence Systems
SP  - 263
EP  - 280
VL  - 9
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1150000
DO  - 10.1080/18756891.2016.1150000
ID  - Guerrero-Enamorado2016
ER  -