• DocumentCode
    1926766
  • Title

    A Novel Reduction Algorithm Based on Expert Knowledge

  • Author

    Yuan, Junpeng ; Su, Jie ; Su, Cheng

  • Author_Institution
    Inst. of Sci. & Tech. of Inf. of China, Beijing
  • fYear
    2009
  • fDate
    25-27 May 2009
  • Firstpage
    536
  • Lastpage
    540
  • Abstract
    For complex systems and some new technology fields, simply rely on machines learning, the results are not reliable. Absorb the expert knowledge, will help us to more accurately grasp the status and the development of the complex or new fields. This paper presents a novel reduction algorithm that takes into account the knowledge of experts. The attributes set is divided into two subset according to the scores of the expert knowledge: the set of attributes with decisive expert knowledge and the set of attributes with experts´ knowledge for reference, then a novel man-machine cooperative intelligent reduction algorithm (IRAEK, Intelligent Reduction Algorithm based on Expert Knowledge) is proposed to find the minimum reduction based on the different scores of expert knowledge. Finally, the empirical analysis result on the Micro-electromechanical Systems (MEMS) field shows that the IRAEK algorithm is feasible and efficient.
  • Keywords
    expert systems; learning (artificial intelligence); set theory; expert knowledge; machine learning; man-machine cooperative intelligent reduction algorithm; micro-electromechanical system; Conference management; Embedded software; Financial management; Information systems; Knowledge management; Man machine systems; Microelectromechanical systems; Micromechanical devices; Set theory; Software algorithms; Decisive Expert Knowledge; Experts´ Knowledge for Reference; Man-Machine Cooperative; Reduction Algorithm; Rough Set Theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Embedded Software and Systems, 2009. ICESS '09. International Conference on
  • Conference_Location
    Zhejiang
  • Print_ISBN
    978-1-4244-4359-8
  • Type

    conf

  • DOI
    10.1109/ICESS.2009.45
  • Filename
    5066695