Pareto front feature selection: using genetic programming to explore feature space

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

@InProceedings{DBLP:conf/gecco/NeshatianZ09,
  author =       "Kourosh Neshatian and Mengjie Zhang",
  title =        "Pareto front feature selection: using genetic
                 programming to explore feature space",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1027--1034",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570040",
  abstract =     "In this paper we use genetic programming (GP) for
                 feature selection in binary classification tasks.
                 Mathematical expressions built by GP transform the
                 feature space in a way that the relevance of subsets of
                 features can be measured using a simple relevance
                 function. We make some modifications to the standard GP
                 to make it explore large subsets of features when
                 necessary. This is done by increasing the depth limit
                 at run-time and at the same time trying to avoid
                 bloating and overfitting by some control mechanism. We
                 take a filter (non-wrapper) approach to exploring the
                 search space. Unlike most filter methods that usually
                 deal with single features, we explore subsets of
                 features. The solution of the proposed search is a
                 vector of Pareto-front points. Our experiments show
                 that a linear search over this vector can improve the
                 classification performance of classifiers while
                 decreasing their complexity.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang

Citations