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Quality Credit Supervision Research Based on Minimum Dataset: With the Licensed Enterprises in Kunming of China for Case

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  • Qianyanhui Qian

    (Yunnan University of Finance and Economics, Kunming, China)

  • Wenqi Liu

    (Kunming University of Science and Technology, Kunming, China)

Abstract

As a social management and public service sector, it is convenient for the government to collect and use data, and hence business data become the origin of the big data of government. In order to improve the scientific level of government supervision and management, it is necessary to strengthen the scientific arrangement by using the existing service data. This paper takes a successful example of implementation of e-government, which is “basic quality and technical supervision and management information system” using by bureau of quality and technical supervision in Kunming. After obtaining the quality information from the original public database, this paper used the thinking model which combining the quality credit information and the minimum dataset, then choosing candidate factors with importance rating from minimum dataset by using R language and random forest algorithm. At last, forming and analyzing the minimum dataset of enterprise quality credit supervision through logical argument.

Suggested Citation

  • Qianyanhui Qian & Wenqi Liu, 2016. "Quality Credit Supervision Research Based on Minimum Dataset: With the Licensed Enterprises in Kunming of China for Case," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 5(2), pages 64-73, April.
    • Handle: RePEc:igg:jfsa00:v:5:y:2016:i:2:p:64-73
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