Improving induction decision trees with parallel genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Gianluigi Folino and Clara Pizzuti and 
                 Giandomenico Spezzano",
  title =        "Improving induction decision trees with parallel
                 genetic programming",
  booktitle =    "Proceedings 10th Euromicro Workshop on Parallel,
                 Distributed and Network-based Processing",
  year =         "2002",
  pages =        "181--187",
  address =      "Canary Islands",
  month =        "9-11 " # jan,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 decision trees, learning by example, parallel
                 programming, J-measure, UCI machine learning
                 repository, fitness function, genetic operators, grid
                 model, induction decision trees, large data sets,
                 parallel genetic programming",
  DOI =          "doi:10.1109/EMPDP.2002.994264",
  abstract =     "A parallel genetic programming approach to induce
                 decision trees in large data sets is presented. A
                 population of trees is evolved by employing the genetic
                 operators and every individual is evaluated by using a
                 fitness function based on the J-measure. The method is
                 able to deal with large data sets since it uses a
                 parallel implementation of genetic programming through
                 the grid model. Experiments on data sets from the UCI
                 machine learning repository show better results with
                 respect to C5. Furthermore, performance results show a
                 nearly linear speedup",
  notes =        "Inspec Accession Number: 7205091",

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano