Classification of Cardiac Arrhythmia by Random Forests with Features Constructed by Kaizen Programming using Linear Genetic Programming

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@InProceedings{Sotto:2016:GECCO,
  author =       "Leo Francoso Dal Piccol Sotto and 
                 Regina Celia Coelho and Vinicius Veloso {de Melo}",
  title =        "Classification of Cardiac Arrhythmia by Random Forests
                 with Features Constructed by Kaizen Programming using
                 Linear Genetic Programming",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "813--820",
  keywords =     "genetic algorithms, genetic programming, Integrative
                 Genetic and Evolutionary Computation",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908882",
  abstract =     "Cardiac rhythm disorders may cause severe heart
                 diseases, stroke, and even sudden cardiac death. Some
                 arrhythmias are so serious that can cause injury to
                 other organs, for instance, brain, kidneys, lungs or
                 liver. Therefore, early and correct diagnosis of
                 cardiac arrhythmia is essential to the prevention of
                 serious problems. There are expert systems to classify
                 arrhythmias from electrocardiograms signals. However,
                 it has been shown that not only selecting the correct
                 features from the dataset but also generating combined
                 features could be the key to having real progress in
                 classification. Therefore, this paper investigates a
                 novel hybrid evolutionary technique to perform both
                 tasks at the same time, finding complementary features
                 that cover different characteristics of the data. The
                 new features were tested with a widely-used classifier
                 called Random Forests. The method reduced a dataset
                 with 279 attributes to 26 attributes and achieved
                 accuracies of 86.39percent for binary classification
                 and 77.69percent for multiclass. Our approach
                 outperformed several popular feature selection, feature
                 generation, and state-of-the-art related work from the
                 literature.",
  notes =        "Universidade Federal de So Paulo

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Regina Celia Coelho Vinicius Veloso de Melo

Citations