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International Symposium on Neural Networks

ISNN 2010: Advances in Neural Networks - ISNN 2010 pp 373–380Cite as

Semivariance Criteria for Quantifying the Choice among Uncertain Outcomes

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

Abstract

In a stochastic decision system, mean-risk is an approach frequently used for modeling the choice among random outcomes, the method quantifies a risk management problem by two criteria (i.e., mean and risk) with possible trade-off analysis. In the literature, there are different risk definitions for a random variable such as variance, critical probability and stochastic dominance. This paper presents semivariance of fuzzy random variable as a new risk criteria for measuring hybrid uncertain outcomes. Since the semivariance is defined by nonlinear fuzzy integral, its computation is a challenge issue for research, and usually depends on intelligent algorithms. This paper will develop some useful semivariance formulas for common triangular and trapezoidal fuzzy random variables, which have potential applications in various practical risk management problems.

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Author information

Authors and Affiliations

  1. College of Mathematics & Computer Science, Hebei University, Baoding, 071002, Hebei, China

    Yankui Liu & Xiaoqing Wang

Authors
  1. Yankui Liu
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  2. Xiaoqing Wang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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Liu, Y., Wang, X. (2010). Semivariance Criteria for Quantifying the Choice among Uncertain Outcomes. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_48

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  • DOI: https://doi.org/10.1007/978-3-642-13278-0_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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