Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification

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

@InProceedings{LaCava:2017:evoApplications,
  author =       "William {La Cava} and Sara Silva and 
                 Leonardo Vanneschi and Lee Spector and Jason Moore",
  title =        "Genetic Programming Representations for
                 Multi-dimensional Feature Learning in Biomedical
                 Classification",
  booktitle =    "20th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2017",
  editor =       "Giovanni Squillero",
  series =       "LNCS",
  volume =       "10199",
  publisher =    "Springer",
  pages =        "158--173",
  address =      "Amsterdam",
  month =        "19-21 " # apr,
  organisation = "Species",
  keywords =     "genetic algorithms, genetic programming, Feature
                 learning, Classification",
  DOI =          "doi:10.1007/978-3-319-55849-3_11",
  abstract =     "We present a new classification method that uses
                 genetic programming (GP) to evolve feature
                 transformations for a deterministic, distanced-based
                 classifier. This method, called M4GP, differs from
                 common approaches to classifier representation in GP in
                 that it does not enforce arbitrary decision boundaries
                 and it allows individuals to produce multiple outputs
                 via a stack-based GP system. In comparison to typical
                 methods of classification, M4GP can be advantageous in
                 its ability to produce readable models. We conduct a
                 comprehensive study of M4GP, first in comparison to
                 other GP classifiers, and then in comparison to six
                 common machine learning classifiers. We conduct full
                 hyper-parameter optimization for all of the methods on
                 a suite of 16 biomedical data sets, ranging in size and
                 difficulty. The results indicate that M4GP outperforms
                 other GP methods for classification. M4GP performs
                 competitively with other machine learning methods in
                 terms of the accuracy of the produced models for most
                 problems. M4GP also exhibits the ability to detect
                 epistatic interactions better than the other methods.",
  notes =        "EvoApplications2017 held in conjunction with
                 EuroGP'2017, EvoCOP2017 and EvoMusArt2017
                 http://www.evostar.org/2017/cfp_evoapps.php.",
}

Genetic Programming entries for William La Cava Sara Silva Leonardo Vanneschi Lee Spector Jason H Moore

Citations