Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Barros:2014:ieeeTEC,
  author =       "Rodrigo C. Barros and Marcio P. Basgalupp and 
                 Alex A. Freitas and Andre C. P. L. F. {de Carvalho}",
  title =        "Evolutionary Design of Decision-Tree Algorithms
                 Tailored to Microarray Gene Expression Data Sets",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2014",
  month =        dec,
  volume =       "18",
  number =       "6",
  pages =        "873--892",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2013.2291813",
  size =         "20 pages",
  abstract =     "Decision-tree induction algorithms are widely used in
                 machine learning applications in which the goal is to
                 extract knowledge from data and present it in a
                 graphically intuitive way. The most successful strategy
                 for inducing decision trees is the greedy top-down
                 recursive approach, which has been continuously
                 improved by researchers over the past 40 years. In this
                 paper, we propose a paradigm shift in the research of
                 decision trees: instead of proposing a new manually
                 designed method for inducing decision trees, we propose
                 automatically designing decision-tree induction
                 algorithms tailored to a specific type of
                 classification data set (or application domain).
                 Following recent breakthroughs in the automatic design
                 of machine learning algorithms, we propose a
                 hyper-heuristic evolutionary algorithm called
                 hyper-heuristic evolutionary algorithm for designing
                 decision-tree algorithms (HEAD-DT) that evolves design
                 components of top-down decision-tree induction
                 algorithms. By the end of the evolution, we expect
                 HEAD-DT to generate a new and possibly better
                 decision-tree algorithm for a given application domain.
                 We perform extensive experiments in 35 real-world
                 microarray gene expression data sets to assess the
                 performance of HEAD-DT, and compare it with very well
                 known decision-tree algorithms such as C4.5, CART, and
                 REPTree. Results show that HEAD-DT is capable of
                 generating algorithms that significantly outperform the
                 baseline manually designed decision-tree algorithms
                 regarding predictive accuracy and F-measure.",
  notes =        "also known as \cite{6670778}",
}

Genetic Programming entries for Rodrigo C Barros Marcio Porto Basgalupp Alex Alves Freitas Andre Ponce de Leon F de Carvalho

Citations