• DocumentCode
    1785044
  • Title

    ELMDF: A new classification algorithm based on Data Field

  • Author

    Shuliang Wang ; Dakui Wang

  • Author_Institution
    Sch. of Software, Beijing Inst. of Technol., Beijing, China
  • fYear
    2014
  • fDate
    2-5 Nov. 2014
  • Firstpage
    28
  • Lastpage
    33
  • Abstract
    In this paper, a novel classification algorithm, ELMDF (Extreme Learning Machine based on Data Field), is proposed to solve the problem of estimating the number of hidden layer neurons in typical ELM. For constructing ELMDF, a new theory based on data field, FMDF (Fundamental Matrix of Data Field) is proposed in this paper. The breast cancer cell image dataset, and the genome dataset are used to test and illustrate the proposed method. The experimental case demonstrates that ELMDF performs better than other six typical supervised learning algorithms on different datasets.
  • Keywords
    bioinformatics; cancer; cellular biophysics; genomics; image classification; learning (artificial intelligence); medical image processing; ELMDF; FMDF; breast cancer cell image dataset; classification algorithm based on data field; extreme learning machine based on data field; fundamental matrix of data field; genome dataset; hidden layer neurons; supervised learning algorithms; Accuracy; Bioinformatics; Breast cancer; Classification algorithms; Genomics; Neurons; Potential energy; Data Field; ELM; ELMDF; FMDF; Neural Network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on
  • Conference_Location
    Belfast
  • Type

    conf

  • DOI
    10.1109/BIBM.2014.6999278
  • Filename
    6999278