Ranking Method

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 79248 Experts worldwide ranked by ideXlab platform

Y. Şubaş - One of the best experts on this subject based on the ideXlab platform.

  • A Ranking Method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems
    International Journal of Machine Learning and Cybernetics, 2017
    Co-Authors: Irfan Deli, Y. Şubaş
    Abstract:

    The concept of a single valued neutrosophic number (SVN-number) is of importance for quantifying an ill-known quantity and the Ranking of SVN-numbers is a very difficult problem in multi-attribute decision making problems. The aim of this paper is to present a Methodol-ogy for solving multi-attribute decision making problems with SVN-numbers. Therefore, we firstly defined the con-cepts of cut sets of SVN-numbers and then applied to single valued trapezoidal neutrosophic numbers (SVTN-numbers) and triangular neutrosophic numbers (SVTrN-numbers). Then, we proposed the values and ambiguities of the truth-membership function, indeterminacy-membership function and falsity-membership function for a SVN-numbers and studied some desired properties. Also, we developed a Ranking Method by using the concept of values and ambiguities, and applied to multi-attribute decision making problems in which the ratings of alternatives on attributes are expressed with SVTN-numbers.

Irfan Deli - One of the best experts on this subject based on the ideXlab platform.

  • A Ranking Method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems
    International Journal of Machine Learning and Cybernetics, 2017
    Co-Authors: Irfan Deli, Y. Şubaş
    Abstract:

    The concept of a single valued neutrosophic number (SVN-number) is of importance for quantifying an ill-known quantity and the Ranking of SVN-numbers is a very difficult problem in multi-attribute decision making problems. The aim of this paper is to present a Methodol-ogy for solving multi-attribute decision making problems with SVN-numbers. Therefore, we firstly defined the con-cepts of cut sets of SVN-numbers and then applied to single valued trapezoidal neutrosophic numbers (SVTN-numbers) and triangular neutrosophic numbers (SVTrN-numbers). Then, we proposed the values and ambiguities of the truth-membership function, indeterminacy-membership function and falsity-membership function for a SVN-numbers and studied some desired properties. Also, we developed a Ranking Method by using the concept of values and ambiguities, and applied to multi-attribute decision making problems in which the ratings of alternatives on attributes are expressed with SVTN-numbers.

Jerry M. Mendel - One of the best experts on this subject based on the ideXlab platform.

  • A comparative study of Ranking Methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets
    Information Sciences, 2009
    Co-Authors: Jerry M. Mendel
    Abstract:

    Ranking Methods, similarity measures and uncertainty measures are very important concepts for interval type-2 fuzzy sets (IT2 FSs). So far, there is only one Ranking Method for such sets, whereas there are many similarity and uncertainty measures. A new Ranking Method and a new similarity measure for IT2 FSs are proposed in this paper. All these Ranking Methods, similarity measures and uncertainty measures are compared based on real survey data and then the most suitable Ranking Method, similarity measure and uncertainty measure that can be used in the computing with words paradigm are suggested. The results are useful in understanding the uncertainties associated with linguistic terms and hence how to use them effectively in survey design and linguistic information processing.

Tao Zhou - One of the best experts on this subject based on the ideXlab platform.

  • evaluating user reputation in online rating systems via an iterative group based Ranking Method
    Physica A-statistical Mechanics and Its Applications, 2017
    Co-Authors: Tao Zhou
    Abstract:

    Reputation is a valuable asset in online social lives and it has drawn increased attention. Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation in online rating systems is especially significant. However, most of the previous Ranking-based Methods either follow a debatable assumption or have unsatisfied robustness. In this paper, we propose an iterative group-based Ranking Method by introducing an iterative reputation–allocation process into the original group-based Ranking Method. More specifically, the reputation of users is calculated based on the weighted sizes of the user rating groups after grouping all users by their rating similarities, and the high reputation users’ ratings have larger weights in dominating the corresponding user rating groups. The reputation of users and the user rating group sizes are iteratively updated until they become stable. Results on two real data sets with artificial spammers suggest that the proposed Method has better performance than the state-of-the-art Methods and its robustness is considerably improved comparing with the original group-based Ranking Method. Our work highlights the positive role of considering users’ grouping behaviors towards a better online user reputation evaluation.

  • Group-based Ranking Method for online rating systems with spamming attacks
    EPL, 2015
    Co-Authors: Yu-wei Dong, Mingsheng Shang, Tao Zhou
    Abstract:

    The Ranking problem has attracted much attention in real systems. How to design a robust Ranking Method is especially significant for online rating systems under the threat of spamming attacks. By building reputation systems for users, many well-performed Ranking Methods have been applied to address this issue. In this letter, we propose a group-based Ranking Method that evaluates users' reputations based on their grouping behaviors. More specifically, users are assigned with high reputation scores if they always fall into large rating groups. Results on three real data sets indicate that the present Method is more accurate and robust than the correlation-based Method in the presence of spamming attacks.

  • Group-based Ranking Method for online rating systems with spamming attacks
    EPL (Europhysics Letters), 2015
    Co-Authors: Jian Gao, Mingsheng Shang, Yu-wei Dong, Shi-min Cai, Tao Zhou
    Abstract:

    Ranking problem has attracted much attention in real systems. How to design a robust Ranking Method is especially significant for online rating systems under the threat of spamming attacks. By building reputation systems for users, many well-performed Ranking Methods have been applied to address this issue. In this Letter, we propose a group-based Ranking Method that evaluates users' reputations based on their grouping behaviors. More specifically, users are assigned with high reputation scores if they always fall into large rating groups. Results on three real data sets indicate that the present Method is more accurate and robust than correlation-based Method in the presence of spamming attacks.

Alex Aussem - One of the best experts on this subject based on the ideXlab platform.

  • A Semi Supervised Feature Ranking Method with Ensemble Learning
    Pattern Recognition Letters, 2012
    Co-Authors: Fazia Bellal, Haytham Elghazel, Alex Aussem
    Abstract:

    We consider the problem of using a large amount of unlabeled data to improve the efficiency of feature selection in high-dimension when only a small amount of labeled examples is available. We propose a new Method called semi-supervised ensemble learning guided feature Ranking Method (SEFR for short), that combines a bagged ensemble of standard semi-supervised approaches with a permutation-based out-of-bag feature importance measure that takes into account both labeled and unlabeled data. We provide empirical results on several benchmark data sets indicating that SEFR can lead to significant improvement over state-of-the-art supervised and semi-supervised algorithms.