• DocumentCode
    3166040
  • Title

    Sparse fuzzy techniques improve machine learning

  • Author

    Sanchez, Ricardo ; Servin, Christian ; Argaez, Miguel

  • Author_Institution
    Comput. Sci. Program, Univ. of Texas at El Paso, El Paso, TX, USA
  • fYear
    2013
  • fDate
    24-28 June 2013
  • Firstpage
    531
  • Lastpage
    535
  • Abstract
    On the example of diagnosing cancer based on the microarray gene expression data, we show that fuzzy-technique description of imprecise knowledge can improve the efficiency of the existing machine learning algorithms. Specifically, we show that the fuzzy-technique description leads to a formulation of the learning problem as a problem of sparse optimization, and we use l1-techniques to solve the resulting optimization problem.
  • Keywords
    cancer; fuzzy set theory; genetics; learning (artificial intelligence); optimisation; patient diagnosis; cancer diagnosis; fuzzy-technique description; imprecise knowledge; l1-techniques; learning problem; machine learning algorithms; microarray gene expression data; optimization problem; sparse fuzzy techniques; sparse optimization; Cancer; Educational institutions; Gene expression; Optimization; Support vector machines; Tumors; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint
  • Conference_Location
    Edmonton, AB
  • Type

    conf

  • DOI
    10.1109/IFSA-NAFIPS.2013.6608456
  • Filename
    6608456
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3166040