• DocumentCode
    624534
  • Title

    Three-way decisions with artificial neural networks

  • Author

    Xiaofei Deng

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Regina, Regina, SK, Canada
  • fYear
    2013
  • fDate
    5-8 May 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The theory of three-way decisions provides an additional option to the conventional two-way decisions that use only two options, namely, accepting or rejecting. The third option is called a non-commitment decision that usually means a decision in deferment or requiring further observations. Recent studies provide an evaluation-based framework of three-way decisions, in which one can make a decision according to evaluations. One of the fundamental issues of this framework is the interpretation and construction of evaluation functions. The Artificial Neural Networks (ANNs) provide a practical method of learning evaluation functions from the training data. Mutual benefits can be found in both theories. The theory of three-way decisions extends the ANNs to a three-valued output model, on the other hand, the ANNs provide a general approach to the construction of evaluations.
  • Keywords
    decision theory; learning (artificial intelligence); neural nets; ANN; accepting option; artificial neural network; evaluation function construction; evaluation function interpretation; evaluation-based framework; learning evaluation function; noncommitment decision; rejecting option; three-valued output model; three-way decision theory; two-way decision theory; Computers; Educational institutions; Neurons; Probabilistic logic; Rough sets; Training; Vectors; Three-way decisions; artificial neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on
  • Conference_Location
    Regina, SK
  • ISSN
    0840-7789
  • Print_ISBN
    978-1-4799-0031-2
  • Electronic_ISBN
    0840-7789
  • Type

    conf

  • DOI
    10.1109/CCECE.2013.6567830
  • Filename
    6567830