Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{pappa:2009a,
  author =       "Gisele L. Pappa and Alex A. Freitas",
  title =        "Automatically evolving rule induction algorithms
                 tailored to the prediction of postsynaptic activity in
                 proteins",
  journal =      "Intelligent Data Analysis",
  year =         "2009",
  volume =       "13",
  number =       "2",
  pages =        "243--259",
  keywords =     "genetic algorithms, genetic programming, Rule
                 induction algorithms, genetic programming, postsynaptic
                 proteins, classification",
  ISSN =         "1088-467X",
  URL =          "http://iospress.metapress.com/content/b72u26327p8720m8/?p=279e0bf5ba1444439e3158730223ce36&pi=4",
  DOI =          "doi:10.3233/IDA-2009-0366",
  abstract =     "It is well-known that no classification algorithm is
                 the best in all application domains. The conventional
                 approach for coping with this problem consists of
                 trying to select the best classification algorithm for
                 the target application domain. We propose a refreshing
                 departure from this approach, consisting of
                 automatically creating a rule induction algorithm
                 tailored to the target application domain. This work
                 proposes a grammar-based genetic programming (GGP)
                 system to perform 'algorithm construction'. The GGP is
                 used to build a complete rule induction algorithm
                 tailored to 5 well-known UCI data sets and a protein
                 data set, where the goal is to predict whether or not a
                 protein presents postsynaptic activity. The results
                 show that the rule induction algorithms automatically
                 constructed by the GGP are competitive with well-known
                 human-designed rule induction algorithms. Moreover, in
                 the postsynaptic case study, the GGP was more
                 successful than the human-designed algorithms in
                 discovering accurate rules predicting the minority
                 class whose prediction is more difficult and tends to
                 be more important to the user than the prediction of
                 the majority class.",
}

Genetic Programming entries for Gisele L Pappa Alex Alves Freitas

Citations