Yazar "Liang, Yingying" seçeneğine göre listele

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    Information Granule Based Uncertainty Measure of Fuzzy Evidential Distribution
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Zhou, Qianli; Pedrycz, Witold; Liang, Yingying; Deng, Yong
    Quantifying the uncertainty of information distributions containing randomness, imprecision, and fuzziness is the premise of processing them. A useful information representation in the field of intelligent computing are information granules, which optimize data from the perspective of specificity and coverage. We introduce information granularity into evidential information and model the basic probability assignment (BPA) as a weighted information granules model. Based on the proposed model, a new uncertainty measure of BPA is derived from the quality evaluation of granules. In addition, the proposed measure is extended to fuzzy evidential information distributions. When the Fuzzy BPA (FBPA) degenerates into the Probability Mass Function (ProbMF) and Possibility Mass Function (PossMF), the proposed method degenerates to Gini entropy and Yager's specificity measure, respectively. We use a refined belief structure to interpret the meaning of FBPA in the transfer belief model, and verify the validity of the proposed method by analyzing its properties and presenting numerical examples. The concept of information granule is used for the first time to model focal set and beliefs. Compared with Shannon entropy based information measures, the proposed method provides a novel perspective on the relationship between randomness, imprecision, and fuzziness in FBPA.
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    Minimum cost consensus model with altruistic preference
    (Pergamon-Elsevier Science Ltd, 2023) Liang, Yingying; Ju, Yanbing; Tu, Yan; Pedrycz, Witold; Martinez, Luis
    The minimum cost consensus model (MCCM) aims at reaching group consensus for either conflict or polarized opinions evaluated in group decision making. Decision makers (DMs) consider both their own and other interests in real-world minimum cost consensus problems, exhibiting the altruistic preference behavior. To quantify the behavior, we define a satisfaction degree function to reflect the interaction among DMs. On this basis, the MCCM with altruistic preference (MCCM-AP) is built, in which a novel consensus measurement for concentrated or scattered opinions is introduced. Furthermore, the MCCM-AP based satisfaction improvement model is established. Finally, an illustrative example and a practical case study are carried out to illustrate the performance of the proposed models, together with sensitivity and comparative analyses are conducted to explore the impact of altruistic preference and discuss the merits of our proposal. The findings indicate that total adjustment cost is nonincreasing with the increase of altruistic preference degree, which provides the decision support for managers to handle the altruistic preference behavior and reduce the consensus cost.
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    Optimizing-Information-Granule-Based Consensus Reaching Model in Large-Scale Group Decision Making
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Liang, Yingying; Pedrycz, Witold; Qin, Jindong
    In large-scale group decision making (LSGDM), the consensus result is expected to be realized explicitly through reconciling various preferences provided by decision makers based on their personalized viewpoints. An information-granule-consensus-based decision brings about high flexibility and promising aspects in group decision making. The consensus reaching proposals reported so far paid little attention to the merits of granular computing for managing LSGDM problems. This article concerns an extension of the well-known analytic hierarchy process to the LSGDM scenario using the optimizing-information-granule-based consensus reaching method. The consensus measurement is first quantified using coverage and specificity to derive the optimal cluster using the fuzzy C-means algorithm. Then, based on the optimization model of an information granule leading from numerical to interval representation, a novel construction model of information granule from interval representations to type-2 interval representation is developed, which yields the consistency of the obtained result instead of proceeding with an extra revision. To achieve the desired consensus, a preference modification algorithm is designed to detect the adjusted decision maker and further provide adjustment suggestions following the reference decision maker. Finally, a numeric study illustrates the effectiveness and flexibility of the proposed method.