M3GP: Multiclass Classification with GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Luis Munoz and Sara Silva and Leonardo Trujillo",
  title =        "{M3GP:} Multiclass Classification with {GP}",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "78--91",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Multiple classes, Multidimensional
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_7",
  abstract =     "Data classification is one of the most ubiquitous
                 machine learning tasks in science and engineering.
                 However, Genetic Programming is still not a popular
                 classification methodology, partially due to its poor
                 performance in multiclass problems. The recently
                 proposed Multidimensional Multiclass Genetic
                 Programming algorithm achieved promising results in
                 this area, by evolving mappings of the p-dimensional
                 data into a d-dimensional space, and applying a minimum
                 Mahalanobis distance classifier. Despite good
                 performance, M2GP employs a greedy strategy to set the
                 number of dimensions d for the transformed data, and
                 fixes it at the start of the search, an approach that
                 is prone to locally optimal solutions. This work
                 presents the M3GP algorithm, that stands for M2GP with
                 multidimensional populations. M3GP extends M2GP by
                 allowing the search process to progressively search for
                 the optimal number of new dimensions d that maximise
                 the classification accuracy. Experimental results show
                 that M3GP can automatically determine a good value for
                 d depending on the problem, and achieves excellent
                 performance when compared to state-of-the-art-methods
                 like Random Forests, Random Subspaces and Multilayer
                 Perceptron on several benchmark and real-world
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and

Genetic Programming entries for Luis Munoz Delgado Sara Silva Leonardo Trujillo