PGGP: Prototype Generation via Genetic Programming

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@Article{Escalante:2016:ASC,
  author =       "Hugo Jair Escalante and Mario Graff and 
                 Alicia Morales-Reyes",
  title =        "PGGP: Prototype Generation via Genetic Programming",
  journal =      "Applied Soft Computing",
  volume =       "40",
  pages =        "569--580",
  year =         "2016",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2015.12.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494615007942",
  abstract =     "Prototype generation (PG) methods aim to find a subset
                 of instances taken from a large training data set, in
                 such a way that classification performance (commonly,
                 using a 1NN classifier) when using prototypes is equal
                 or better than that obtained when using the original
                 training set. Several PG methods have been proposed so
                 far, most of them consider a small subset of training
                 instances as initial prototypes and modify them trying
                 to maximize the classification performance on the whole
                 training set. Although some of these methods have
                 obtained acceptable results, training instances may be
                 under-exploited, because most of the times they are
                 only used to guide the search process. This paper
                 introduces a PG method based on genetic programming in
                 which many training samples are combined through
                 arithmetic operators to build highly effective
                 prototypes. The genetic program aims to generate
                 prototypes that maximize an estimate of the
                 generalization performance of an 1NN classifier.
                 Experimental results are reported on benchmark data to
                 assess PG methods. Several aspects of the genetic
                 program are evaluated and compared to many alternative
                 PG methods. The empirical assessment shows the
                 effectiveness of the proposed approach outperforming
                 most of the state of the art PG techniques when using
                 both small and large data sets. Better results were
                 obtained for data sets with numeric attributes only,
                 although the performance of the proposed technique on
                 mixed data was very competitive as well.",
  keywords =     "genetic algorithms, genetic programming, Prototype
                 generation, 1NN classification, Pattern
                 classification",
}

Genetic Programming entries for Hugo Jair Escalante Mario Graff Guerrero Alicia Morales-Reyes

Citations