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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (6): 48-57    DOI: 10.11925/infotech.2096-3467.2017.1124
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Impacts and Corrections of Natural Weight on Nonlinear Sci-tech Reviews——Case Study of TOPSIS Method
Liping Yu1,3,Xiayun Song2,Zuogong Wang3()
1School of Management and e-Business, Zhejiang Gongshang University, Hangzhou 310018, China
2School of Accounting, Zhejiang University of Finance and Economics, Hangzhou 310018, China
3Finance School, Guizhou University of Finance and Economics, Guiyang 550025, China
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[Objective] This paper explores the implicit natural weight issues facing the scientific and technology review indexes, and then proposes a method to address them. [Methods] First, we analyzed data from the JCR2016 mathematics journals with the help of TOPSIS method, aiming to find the influence of natural weights on the nonlinear evaluation method. Then, we proposed a method increasing the dynamic maximum mean to the standardized level, aiming to eliminate the impacts. [Results] We found that the natural weights posed significant effects to the Nonlinear Evaluation methods. For the weighted method, the design weights, the natural weights and the evaluation methods all affected the actual weights. For the non-weighted method, the natural weights and the evaluation methods affected the actual weights. Eliminating the natural weights could effectively reduce the influence of the evaluation method on the actual weights, which helps the design weights play a bigger role. The distribution of index data also affected the actual weights. [Limitations] The proposed method is still an approximation algorithm, which could not yield the exactly equal means. [Conclusions] To achieve the fair review for the science and technology products, we must pay attention to the natural weights issues, which is a systematic error.

Key wordsNatural Weights      Design Weights      Standardization Method of Approximate      Dynamic Maximum Mean      Actual Weights      Nonlinear Evaluation Method     
Received: 14 November 2017      Published: 11 July 2018

Cite this article:

Liping Yu,Xiayun Song,Zuogong Wang. Impacts and Corrections of Natural Weight on Nonlinear Sci-tech Reviews——Case Study of TOPSIS Method. Data Analysis and Knowledge Discovery, 2018, 2(6): 48-57.

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