Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Daniel Rivero and Juan R. Rabunal and 
                 Julian Dorado and Alejandro Pazos",
  title =        "Automatic Design of ANNs by Means of GP for Data
                 Mining Tasks: Iris Flower Classification Problem",
  year =         "2007",
  bibsource =    "DBLP,",
  booktitle =    "8th International Conference on Adaptive and Natural
                 Computing Algorithms, ICANNGA 2007, Part I",
  editor =       "Bartlomiej Beliczynski and Andrzej Dzielinski and 
                 Marcin Iwanowski and Bernardete Ribeiro",
  series =       "Lecture Notes in Computer Science",
  volume =       "4431",
  pages =        "276--285",
  address =      "Warsaw, Poland",
  month =        apr # " 11-14",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-71589-4",
  DOI =          "doi:10.1007/978-3-540-71618-1_31",
  abstract =     "This paper describes a new technique for automatically
                 developing Artificial Neural Networks (ANNs) by means
                 of an Evolutionary Computation (EC) tool, called
                 Genetic Programming (GP). This paper also describes a
                 practical application in the field of Data Mining. This
                 application is the Iris flower classification problem.
                 This problem has already been extensively studied with
                 other techniques, and therefore this allows the
                 comparison with other tools. Results show how this
                 technique improves the results obtained with other
                 techniques. Moreover, the obtained networks are simpler
                 than the existing ones, with a lower number of hidden
                 neurons and connections, and the additional advantage
                 that there has been a discrimination of the input
                 variables. As it is explained in the text, this
                 variable discrimination gives new knowledge to the
                 problem, since now it is possible to know which
                 variables are important to achieve good results.",
  notes =        "ICANNGA 2007",

Genetic Programming entries for Daniel Rivero Cebrian Juan Ramon Rabunal Dopico Julian Dorado Alejandro Pazos Sierra