An Algorithm Evaluation for Discovering Classification Rules with Gene Expression Programming

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