Weighted Multi-Granularity Rough Set And Its Application In E-Commerce Platform Enterprise Credit Evaluation

With the rapid development of online transactions and Internet technology,e-commerce platforms have opened up profit channels for enterprises,but they also planted hidden risks of credit risk.Therefore,evaluating credit level of e-commerce platform enterprises effectively and clarifying key influencing factors are necessary measures to ensure a long-term win-win situation for the three parties of enterprises,customers and platforms.At this stage,these enterprises' credit information is sometimes incomplete and there are many interference data.Also,the indicators are relatively single when evaluating credit.Meanwhile,although the classic rough set theory has certain advantages in analyzing noisy data and uncertain problems,it still has limitations.For example,not considering the difference form conditional attributes' importance,and cannot process multi-channel information.Consequently,this article explores causes of credit risk of these enterprises from multiple dimensions,optimizes the classic rough set,and applies improved model to corporate credit evaluation research,so as to screen qualified enterprises for customers,and provide decision basis to safeguard their own rights.Firstly,considering that coverage information from multiple channels may be generated during the evaluation process,and evaluation indicators have high dimensionality and complexity,this paper reconstructs weighted generalized multi-granularity rough set under multi-source coverage information system,and defines calculation method of important measurement function.Then,it discusses model's relevant properties,attribute reduction steps and shows relevant decision rules.It also verifies the model have certain feasibility through case studies.Secondly,in the context of e-commerce platforms' development and related policies,credit risks' characteristics of enterprises are sorted out,and several factors of enterprises' credit are summarized.Also,representative factors are selected from corporate financial status,management operation and development potential with relevant principles,so as to establish an e-commerce platform enterprise credit evaluation indicator system.Finally,data from twelve companies is randomly selected on an e-commerce platform to conduct an empirical study on credit evaluation.Redundant indicators are eliminated,significant impact factors and corresponding evaluation decision rules are obtained to improve evaluation efficiency through attribute reduction algorithm.Study results prove that reconstructed rough set is practical in e-commerce platform enterprise's credit evaluation information system.It can guide e-commerce platform to strengthen the supervision of settled enterprises in a targeted manner,and urge enterprises to continuously improve and reform,in order to provide customers better products and services.