Finding the tree in the Forest

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

@InProceedings{Konig:2010:IADIS,
  author =       "Rikard Konig and Ulf Johansson and Lars Niklasson",
  title =        "Finding the tree in the Forest",
  booktitle =    "Proceedings of the IADIS International Conference
                 Applied Computing",
  year =         "2010",
  editor =       "Hans Weghorn and Pedro Isaias and Radu Vasiu",
  pages =        "135--142",
  address =      "Timisoara, Romania",
  month =        "14-16 " # oct,
  organisation = "IADIS",
  keywords =     "genetic algorithms, genetic programming, decision
                 support, decision trees, alternative solutions,
                 inconsistency",
  isbn13 =       "978-972-8939-30-4",
  URL =          "http://www.iadisportal.org/applied-computing-2010-proceedings",
  URL =          "http://bada.hb.se/bitstream/2320/6870/1/Finding%20the%20Tree%20In%20The%20forest.pdf",
  size =         "8 pages",
  abstract =     "Decision trees are often used for decision support
                 since they are fast to train, easy to understand and
                 deterministic; i.e., always create identical trees from
                 the same training data. This property is, however, only
                 inherent in the actual decision tree algorithm,
                 nondeterministic techniques such as genetic programming
                 could very well produce different trees with similar
                 accuracy and complexity for each execution. Clearly, if
                 more than one solution exists, it would be misleading
                 to present a single tree to a decision maker. On the
                 other hand, too many alternatives could not be handled
                 manually, and would only lead to confusion. Hence, we
                 argue for a method aimed at generating a suitable
                 number of alternative decision trees with comparable
                 accuracy and complexity. When too many alternative
                 trees exist, they are grouped and representative
                 accurate solutions are selected from each group. Using
                 domain knowledge, a decision maker could then select a
                 single best tree and, if required, be presented with a
                 small set of similar solutions, in order to further
                 improve his decisions. In this paper, a method for
                 generating alternative decision trees is suggested and
                 evaluated. All in all,four different techniques for
                 selecting accurate representative trees from groups of
                 similar solutions are presented. Experiments on 19 UCI
                 data sets show that it often exist dozens of
                 alternative trees, and that one of the evaluated
                 techniques clearly outperforms all others for selecting
                 accurate and representative models.",
}

Genetic Programming entries for Rikard Konig Ulf Johansson Lars Niklasson

Citations