GEML: A Grammatical Evolution, Machine Learning Approach to Multi-class Classification

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@InProceedings{Fitzgerald:2015:ECTArevised,
  author =       "Jeannie M. Fitzgerald and R. Muhammad Atif Azad and 
                 Conor Ryan",
  title =        "GEML: A Grammatical Evolution, Machine Learning
                 Approach to Multi-class Classification",
  booktitle =    "The 7th International Joint Conference on
                 Computational Intelligence (IJCCI 2015)",
  year =         "2015",
  editor =       "Juan Julian Merelo and Agostinho Rosa and 
                 Jose M. Cadenas and Antonio Dourado Correia and 
                 Kurosh Madani and Antonio Ruano and Joaquim Filipe",
  volume =       "669",
  series =       "Studies in Computational Intelligence",
  pages =        "113--134",
  address =      "Lisbon, Portugal",
  month =        nov # " 12-14",
  organisation = "INSTICC",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Multi-class classification, Evolutionary
                 computation, Machine learning",
  isbn13 =       "978-3-319-48506-5",
  DOI =          "doi:10.1007/978-3-319-48506-5_7",
  abstract =     "In this paper, we propose a hybrid approach to solving
                 multi-class problems which combines evolutionary
                 computation with elements of traditional machine
                 learning. 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. We investigate the effectiveness of GEML on
                 several supervised, semi-supervised and unsupervised
                 multi-class problems and demonstrate its competitive
                 performance when compared with several well known
                 machine learning algorithms. The GEML framework evolves
                 human readable solutions which provide an explanation
                 of the logic behind its classification decisions,
                 offering a significant advantage over existing
                 paradigms for unsupervised and semi-supervised
                 learning. In addition we also examine the possibility
                 of improving the performance of the algorithm through
                 the application of several ensemble techniques.",
  notes =        "Published by Springer 2017. See
                 \cite{Fitzgerald:2015:ECTA}

                 Biocomputing and Developmental Systems Group,
                 University of Limerick, Limerick, Ireland",
}

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

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