An Algorithm Evaluation for Discovering Classification Rules with Gene Expression Programming
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- @Article{Guerrero-Enamorado:2016:IJCIS,
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author = "Alain Guerrero-Enamorado and Carlos Morell and
Amin Y. Noaman and Sebastian Ventura",
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title = "An Algorithm Evaluation for Discovering Classification
Rules with Gene Expression Programming",
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journal = "International Journal of Computational Intelligence
Systems",
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year = "2016",
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volume = "9",
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number = "2",
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pages = "263--280",
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, classification rules,
discriminant functions",
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DOI = "doi:10.1080/18756891.2016.1150000",
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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).",
- }
Genetic Programming entries for
Alain Guerrero-Enamorado
Carlos Morell
Amin Yousef Mohammad Noaman
Sebastian Ventura
Citations