• DocumentCode
    554014
  • Title

    Notice of Retraction
    Choquet integral regression algorithm based on Liu´s generalized Lambda-measure and N-density

  • Author

    Hsiang-Chuan Liu ; Chuan-Yuan Chen

  • Author_Institution
    Dept. of Bioinf. & Med. Inf., Asia Univ., Taichung, Taiwan
  • Volume
    1
  • fYear
    2011
  • fDate
    26-28 July 2011
  • Firstpage
    353
  • Lastpage
    357
  • Abstract
    Notice of Retraction

    After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

    We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

    The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

    In this paper, a novel Choquet integral regression algorithm based on Liu´s generalized Lambda measure and the new fuzzy density, N-density is proposed. A real data set about Students Valuing Science with 5- fold cross-validation MSE is conducted, for comparing the performances of this new algorithm with the Choquet integral regression algorithm based on two well-known fuzzy measures, P-measure and lambda-measure, and two fuzzy density function, R-density and N-density, respectively, and two traditional regression model, multiple regression model and ridge regression model, the results show that the proposed new algorithm has the best performance.
  • Keywords
    fuzzy set theory; integral equations; regression analysis; Choquet integral regression algorithm; Liu´s generalized Lambda-measure; N-density; R-density; data set; fuzzy density function; Classification algorithms; Computational modeling; Data models; Density functional theory; Density measurement; Educational institutions; Forecasting; Choquet integral; Lambda- measure; N-density; fuzzy density; fuzzy measure;
  • 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.2011.6022124
  • Filename
    6022124