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Title Using CV-CRITIC to Determine Weights for Smart City Evaluation
ID_Doc 45641
Authors Yin, QY; Niu, K; Li, N
Title Using CV-CRITIC to Determine Weights for Smart City Evaluation
Year 2017
Published
DOI 10.1109/ICTAI.2017.00153
Abstract With the continuous expansion and deepening of smart city construction, research has focused on establishing the best method for evaluating the level of smart city development. Because of the direct influence of index weight on the evaluation results, determining index weights for smart city evaluation is a challenging and strategically important problem. Generally, previous research has used the expert scoring method or the entropy method to determine the weights of indexes in the evaluation of smart cities. However, the number of samples used by these methods is far fewer than optimal, and these methods have provided low accuracy and poor performance. In addition, expert scoring leads to results that are strongly subjective, and it fails to consider the conflict between indexes that always accompanies the process of determining weights using traditional methods. To address these issues, we proposed CV-CRITIC, an index weight determination method based on the CRITIC method that can be used effectively in smart city evaluation. Use of an objective weighting method such as CRITIC can reduce the effect of subjective factors. The CV-CRITIC method used an improved weighted algorithm based on objective data that considered the influence of two factors on the index weights: the information contained in the index itself, and the conflict between indicators. In addition, we introduced the Coefficient of Variance method into the index weight determination process to analyze the important differences between the two factors, and to determine the combined weights. The comparative results of two sets of experiments verified the advantages of our proposed method in terms of objectivity, simplicity and accuracy.
Author Keywords Smart City; Evaluation; CRITIC method; Coefficient of Variance method; CV-CRITIC
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Conference Proceedings Citation Index - Science (CPCI-S)
EID WOS:000435294700142
WoS Category Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic
Research Area Computer Science; Engineering
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