Evolving Accurate and Comprehensible Classification Rules

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Sonstrod:2011:EAaCCR,
  title =        "Evolving Accurate and Comprehensible Classification
                 Rules",
  author =       "Cecilia Sonstrod and Ulf Johansson and Rikard Konig",
  pages =        "1435--1442",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
                 Computation",
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, clustering, data analysis and data
                 mining, Learning classifier systems",
  DOI =          "doi:10.1109/CEC.2011.5949784",
  abstract =     "In this paper, Genetic Programming is used to evolve
                 ordered rule sets (also called decision lists) for a
                 number of benchmark classification problems, with
                 evaluation of both predictive performance and
                 comprehensibility. The main purpose is to compare this
                 approach to the standard decision list algorithm JRip
                 and also to evaluate the use of different length
                 penalties and fitness functions for evolving this type
                 of model. The results, using 25 data sets from the UCI
                 repository, show that genetic decision lists with
                 accuracy-based fitness functions outperform JRip
                 regarding accuracy. Indeed, the best setup was
                 significantly better than JRip. JRip, however, held a
                 slight advantage over these models when evaluating AUC.
                 Furthermore, all genetic decision list setups produced
                 models that were more compact than JRip models, and
                 thus more readily comprehensible. The effect of using
                 different fitness functions was very clear; in essence,
                 models performed best on the evaluation criterion that
                 was used in the fitness function, with a worsening of
                 the performance for other criteria. Brier score fitness
                 provided a middle ground, with acceptable performance
                 on both accuracy and AUC. The main conclusion is that
                 genetic programming solves the task of evolving
                 decision lists very well, but that different length
                 penalties and fitness functions have immediate effects
                 on the results. Thus, these parameters can be used to
                 control the trade-off between different aspects of
                 predictive performance and comprehensibility.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",
}

Genetic Programming entries for Cecilia Sonstrod Ulf Johansson Rikard Konig

Citations