Genetic Programming of Heterogeneous Ensembles for Classification

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

@InProceedings{conf/ciarp/EscalanteAMA13,
  author =       "Hugo Jair Escalante and Niusvel Acosta-Mendoza and 
                 Alicia Morales-Reyes and Andres Gago Alonso",
  title =        "Genetic Programming of Heterogeneous Ensembles for
                 Classification",
  year =         "2013",
  booktitle =    "Proceedings of the 18th Iberoamerican Congress on
                 Image Analysis, Computer Vision, and Applications
                 (CIARP 2013) Part {I}",
  editor =       "Jose Ruiz-Shulcloper and Gabriella Sanniti di Baja",
  volume =       "8258",
  series =       "Lecture Notes in Computer Science",
  pages =        "9--16",
  address =      "Havana, Cuba",
  month =        nov # " 20-23",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2013-11-17",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ciarp/ciarp2013-1.html#EscalanteAMA13",
  isbn13 =       "978-3-642-41821-1",
  URL =          "http://dx.doi.org/10.1007/978-3-642-41822-8",
  size =         "8 pages",
  abstract =     "The ensemble classification paradigm is an effective
                 way to improve the performance and stability of
                 individual predictors. Many ways to build ensembles
                 have been proposed so far, most notably bagging and
                 boosting based techniques. Evolutionary algorithms
                 (EAs) also have been widely used to generate ensembles.
                 In the context of heterogeneous ensembles EAs have been
                 successfully used to adjust weights of base classifiers
                 or to select ensemble members. Usually, a weighted sum
                 is used for combining classifiers outputs in both
                 classical and evolutionary approaches. This study
                 proposes a novel genetic program that learns a fusion
                 function for combining heterogeneous-classifiers
                 outputs. It evolves a population of fusion functions in
                 order to maximise the classification accuracy. Highly
                 non-linear functions are obtained with the proposed
                 method, subsuming the existing weighted-sum
                 formulations. Experimental results show the
                 effectiveness of the proposed approach, which can be
                 used not only with heterogeneous classifiers but also
                 with homogeneous-classifiers and under bagging/boosting
                 based formulations.",
}

Genetic Programming entries for Hugo Jair Escalante Niusvel Acosta-Mendoza Alicia Morales-Reyes Andres Gago Alonso

Citations