Multiclass Classification Through Multidimensinoal Clustering

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{Silva:2015:GPTP,
  author =       "Sara Silva and Luis Munoz and Leonardo Trujillo and 
                 Vijay Ingalalli and Mauro Castelli and 
                 Leonardo Vanneschi",
  title =        "Multiclass Classification Through Multidimensinoal
                 Clustering",
  booktitle =    "Genetic Programming Theory and Practice XIII",
  year =         "2015",
  editor =       "Rick Riolo and William P. Worzel and M. Kotanchek and 
                 A. Kordon",
  series =       "Genetic and Evolutionary Computation",
  pages =        "219--239",
  address =      "Ann Arbor, USA",
  month =        "14-16 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Multiple classes, Clustering",
  isbn13 =       "978-3-319-34223-8",
  URL =          "http://www.springer.com/us/book/9783319342214",
  DOI =          "doi:10.1007/978-3-319-34223-8_13",
  abstract =     "Classification is one of the most important machine
                 learning tasks in science and engineering. However, it
                 can be a difficult task, in particular when a high
                 number of classes is involved. Genetic Programming,
                 despite its recognized successfulness in so many
                 different domains, is one of the machine learning
                 methods that typically struggles, and often fails, to
                 provide accurate solutions for multi-class
                 classification problems. We present a novel algorithm
                 for tree based GP that incorporates some ideas on the
                 representation of the solution space in higher
                 dimensions, and can be generalized to other types of
                 GP. We test three variants of this new approach on a
                 large set of benchmark problems from several different
                 sources, and observe their competitiveness against the
                 most successful state-of-the-art classifiers like
                 Random Forests, Random Subspaces and Multilayer
                 Perceptron.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2015:GPTP} Published after the
                 workshop in 2016",
}

Genetic Programming entries for Sara Silva Luis Munoz Delgado Leonardo Trujillo Vijay Ingalalli Mauro Castelli Leonardo Vanneschi

Citations