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Chinese Journal of Management Science ›› 2020, Vol. 28 ›› Issue (1): 162-169.doi: 10.16381/j.cnki.issn1003-207x.2020.01.014

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A Data Preprocessing Method in Dynamic Comprehensive Evaluation

XU Lin-ming1, LI Mei-juan2   

  1. 1. School of Management, Fujian University of Technology, Fuzhou 350118, China;
    2. School of Economics&Management, Fuzhou University, Fuzhou 350108, China
  • Received:2019-01-09 Revised:2019-07-10 Online:2020-01-20 Published:2020-01-19

Abstract: Data preprocessing is the basis of comprehensive evaluation and it usually includes uniformization of indicator types and nondimensionalization. Since the preprocessing result of indicator values can greatly influence the subsequent evaluation conclusion, whether its processes are appropriate or not has a direct impact on the reasonability of the final conclusion. Focusing on the problem that implicit incremental information will be eliminated when the three-dimensional data in dynamic comprehensive evaluation are processed by existing static data preprocessing methods, a data preprocessing method used in dynamic comprehensive evaluation is put forward to solve this problem.
By introducing and analyzing the characteristics of six preprocessing methods of evaluation indexes, including range conversion method, linear proportional transformation, normalized processing, vector specification, standard sample transformation, efficacy coefficient method, a global improved normalization method (GIN) is proposed as a method for uniformization and nondimensionalization of indicators in dynamic comprehensive evaluation and preprocessing of indicator values.
The idea of GIN method is as follows:it comprehensively considers and uniformly preprocess each index value in every moment, so as to retain the incremental information implied in the original data. The subtraction consistency method is employed for index type consistency processing. After processing, the larger the index value means the better. The difference between the original index values is retained after the dimensionless processing, and the pre-processed index values are comparable. Finally, through comparison and analysis of calculating examples, it illustrates the features of various methods and verifies that the global improved normalization method is an effective data preprocessing method in dynamic comprehensive evaluation.
The GIN method can retain the incremental information of the original data, and can be used to process the three-dimensional data in the dynamic comprehensive evaluation, which is a useful supplement to the comprehensive evaluation method and its application research.

Key words: dynamic comprehensive evaluation, nondimensionalization, uniformization, preprocessing method, evaluation indicator

CLC Number: