• DocumentCode
    554045
  • Title

    New fuzzy k-NN classification by using genetic algorithm

  • Author

    Junli Lu ; Guang Zhao ; Cheng Yang ; Junjia Lu

  • Author_Institution
    Dept. of Math. & Comput. Sci., Yunnan Univ. of Nat., Kunming, China
  • Volume
    2
  • fYear
    2011
  • fDate
    26-28 July 2011
  • Firstpage
    1111
  • Lastpage
    1115
  • Abstract
    Fuzzy k-NN classification is well-known in data mining, and genetic algorithm is ever been applied to calculate the parameter k and m of fuzzy k-NN, named IFKNN. This paper proposes a new fuzzy k-NN classification method by using genetic algorithm(NFKNN), which need less time and increases classification correct rate. We have verified the efficiency of our methods by theoretical analysis and experiments. The experiments are extensive and comprehensive, we compared each improvement with IFKNN, and we also executed the NFKNN on real datasets and obtained the useful results.
  • Keywords
    data mining; fuzzy set theory; genetic algorithms; pattern classification; NFKNN; data mining; genetic algorithm; new fuzzy k-NN classification method; Accuracy; Biological cells; Classification algorithms; Databases; Genetic algorithms; Glass; Training; fuzzy k-NN; genetic algorithm; sample-edge-of-class; sample-inside-class;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2011 Seventh International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    2157-9555
  • Print_ISBN
    978-1-4244-9950-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2011.6022182
  • Filename
    6022182