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A Multiple Criteria Model for Comparison of Subjective-Objective Evaluations and Its Application

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 218))

Abstract

A multiple criteria decision model addressing the comparison of both subjective and objective evaluation results is proposed in this paper. Firstly, based on cluster analysis, a method to select representative sample data set from all alternatives under evaluation is designed; next, experts are invited to review these sample data and dominance-based rough set theory is used to analyze expert decisions in format of a set of decision rules; then, these trained decision rules are applied to all alternatives and hence, an objective-oriented results can be obtained and used to compare with the alternatives’ self-evaluation results which contains subjective orientation; finally, the method is applied to analyze the graduate’s leaning ability to demonstrate its feasibility.

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References

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Acknowledgments

This work was supported in part by the National Natural Sciences Foundation of China through the project entitled “Research for the inverse problem based on the graph model for conflict resolution (71471087) and by the Chinese Society of Academic Degrees and Graduate Education through the project “The empirical study of the graduates’ learning ability in research-oriented university” (B2-2013Y13-176). Also, the authors wish to express their sincere appreciation to anonymous referees whose comments helped us to improve the paper significantly.

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Correspondence to Ye Chen .

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Chen, Y., Li, Y., Sun, W., Xu, H. (2015). A Multiple Criteria Model for Comparison of Subjective-Objective Evaluations and Its Application. In: Kamiński, B., Kersten, G., Szapiro, T. (eds) Outlooks and Insights on Group Decision and Negotiation. GDN 2015. Lecture Notes in Business Information Processing, vol 218. Springer, Cham. https://doi.org/10.1007/978-3-319-19515-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-19515-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19514-8

  • Online ISBN: 978-3-319-19515-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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