A gene expression programming algorithm for discovering classification rules in the multi-objective space

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@Article{Alain18,
  author =       "Alain Guerrero-Enamorado and Carlos Morell and 
                 Sebastian Ventura",
  title =        "A gene expression programming algorithm for
                 discovering classification rules in the multi-objective
                 space",
  journal =      "International Journal of Computational Intelligence
                 Systems",
  volume =       "11",
  number =       "1",
  pages =        "540--559",
  year =         "2018",
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming (GEP), Reference Point Based
                 Multi-objective Evolutionary Algorithm (R-NSGA-II),
                 Multi-objective Evolutionary Algorithm (MOEA),
                 Multi-objective classification, Classification",
  publisher =    "Atlantis Press",
  ISSN =         "1875-6883",
  URL =          "https://www.atlantis-press.com/journals/ijcis/25891989",
  DOI =          "doi:10.2991/ijcis.11.1.40",
  size =         "20 pages",
  abstract =     "Multi-objective evolutionary algorithms have been
                 criticized when they are applied to classification rule
                 mining, and, more specifically, in the optimization of
                 more than two objectives due to their computational
                 complexity. It is known that a multi-objective space is
                 much richer to be explored than a single-objective
                 space. In consequence, there are only few
                 multi-objective algorithms for classification and their
                 empirical assessed is quite limited. On the other hand,
                 gene expression programming has emerged as an
                 alternative to carry out the evolutionary process at
                 genotypic level in a really efficient way. This paper
                 introduces a new multi-objective algorithm for
                 discovering classification rules, AR-NSGEP (Adaptive
                 Reference point based Non-dominated Sorting with Gene
                 Expression Programming). It is a multi-objective
                 evolution of a previous single-objective algorithm. In
                 AR-NSGEP, the multi-objective search was based on the
                 well known R-NSGA-II algorithm, replacing GA with GEP
                 technology. Four objectives led the rules-discovery
                 process, three of them (sensitivity, specificity and
                 precision) were focused on promoting accuracy and the
                 fourth (simpleness) on the interpretability of rules.
                 AR-NSGEP was evaluated on several benchmark data sets
                 and compared against six rule-based classifiers widely
                 used. The AR-NSGEP, with four-objectives, achieved a
                 significant improvement of the AUC metric with respect
                 to most of the algorithms assessed, while the
                 predictive accuracy and number of rules in the obtained
                 models reached to acceptable results.",
}

Genetic Programming entries for Alain Guerrero-Enamorado Carlos Morell Sebastian Ventura

Citations