• DocumentCode
    264277
  • Title

    Genetic programming as a feature selection algorithm

  • Author

    Suarez, Ranyart R. ; Valencia-Ramirez, Jose Maria ; Graff, Mario

  • fYear
    2014
  • fDate
    5-7 Nov. 2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Genetic Programming (GP) is an Evolutionary Algorithm commonly used to evolve computer programs in order to solve a particular task. Therefore, GP has been used to tackle different problems like classification and regression. In this work, the capabilities of GP in other types of problems are explored, particularly the feature selection problem. For this purpose, GP is applied to a set of benchmark problems, and, then, compared to other algorithms. The results obtained show that GP is competitive against the other algorithms, and in addition to this, no modifications are needed to perform the feature extraction task.
  • Keywords
    feature extraction; feature selection; genetic algorithms; regression analysis; benchmark problems; computer programs; evolutionary algorithm; feature extraction task; feature selection algorithm; genetic programming; Accuracy; Algorithm design and analysis; Benchmark testing; Bit error rate; Feature extraction; Noise; Radio frequency;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power, Electronics and Computing (ROPEC), 2014 IEEE International Autumn Meeting on
  • Conference_Location
    Ixtapa
  • Type

    conf

  • DOI
    10.1109/ROPEC.2014.7036345
  • Filename
    7036345