GEML: Evolutionary Unsupervised and Semi-Supervised Learning of Multi-class Classification with Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Fitzgerald:2015:ECTA,
  author =       "Jeannie Fitzgerald and R. Muhammad Atif Azad and 
                 Conor Ryan",
  title =        "GEML: Evolutionary Unsupervised and Semi-Supervised
                 Learning of Multi-class Classification with Grammatical
                 Evolution",
  booktitle =    "ECTA. 7th International Conference on Evolutionary
                 Computation Theory and Practice",
  year =         "2015",
  editor =       "Agostinho Rosa and Juan Julian Merelo and 
                 Antonio Dourado and Jose M. Cadenas and Kurosh Madani and 
                 Antonio Ruano and Joaquim Filipe",
  address =      "Lisbon, Portugal",
  month =        "12-14 " # nov,
  pages =        "83--94",
  organisation = "INSTICC - Institute for Systems and Technologies of
                 Information, Control and Communication, IFAC -
                 International Federation of Automatic Control, IEEE SMC
                 - IEEE Systems, Man and Cybernetics Society",
  publisher =    "SCITEPRESS - Science and Technology Publications",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Semi-supervised Learning, Multi-class
                 Classification, Evolutionary Computation, Machine
                 Learning",
  isbn13 =       "978-9-8975-8165-6",
  URL =          "http://www.researchgate.net/publication/283055687_GEML_Evolutionary_Unsupervised_and_Semi-Supervised_Learning_of_Multi-class_Classification_with_Grammatical_Evolution",
  URL =          "http://ieeexplore.ieee.org/document/7529309/",
  size =         "12 pages",
  abstract =     "This paper introduces a novel evolutionary approach
                 which can be applied to supervised, semi-supervised and
                 unsupervised learning tasks. The method, Grammatical
                 Evolution Machine Learning (GEML), adapts machine
                 learning concepts from decision tree learning and
                 clustering methods, and integrates these into a
                 Grammatical Evolution framework. With minor adaptations
                 to the objective function the system can be trivially
                 modified to work with the conceptually different
                 paradigms of supervised, semi-supervised and
                 unsupervised learning.The framework generates human
                 readable solutions which explain the mechanics behind
                 the classification decisions, offering a significant
                 advantage over existing paradigms for unsupervised and
                 semi-supervised learning. GEML is studied on a range of
                 multi-class classification problems and is shown to be
                 competitive with several state of the art multi-class
                 classification algorithms.",
  notes =        "IJCCI Also known as \cite{7529309}

                 See \cite{Fitzgerald:2015:ECTArevised}",
}

Genetic Programming entries for Jeannie Fitzgerald R Muhammad Atif Azad Conor Ryan

Citations