A study of fitness functions for data classification using grammatical evolution

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@InProceedings{Chareka:2016:PRASA,
  author =       "Tatenda Chareka and Nelishia Pillay",
  booktitle =    "2016 Pattern Recognition Association of South Africa
                 and Robotics and Mechatronics International Conference
                 (PRASA-RobMech)",
  title =        "A study of fitness functions for data classification
                 using grammatical evolution",
  year =         "2016",
  abstract =     "Data classification is a well studied area with
                 various techniques such as support vector machines,
                 decision trees, neural networks and evolutionary
                 algorithms, amongst others successfully applied to this
                 domain. The research presented in this paper forms part
                 of an initiative aimed at evaluating grammatical
                 evolution, a recent variation of genetic programming,
                 for data classification. The paper reports on a study
                 conducted to compare six different measures, namely,
                 accuracy, true positive rate, false positive rate,
                 precision, F-score and Matthew's correlation
                 coefficient, as fitness functions for grammatical
                 evolution. The performance of grammatical evolution
                 using the six measures as a fitness function is
                 evaluated for multi-class data classification. The
                 study has shown that the accuracy and F-score are
                 effective as fitness functions outperforming all other
                 measures. In some instances accuracy produced better
                 results than F-score. Future work will examine the
                 correlation between the characteristics of the data set
                 and the best performing measure.",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  DOI =          "doi:10.1109/RoboMech.2016.7813165",
  month =        nov,
  notes =        "Also known as \cite{7813165}",
}

Genetic Programming entries for Tatenda Chareka Nelishia Pillay

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