Consumer credit evaluation of mobile e-commerce platform based on random forest Online publication date: Wed, 10-Jan-2024
by Ming Yang
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 29, No. 3/4, 2023
Abstract: With the goal of improving recall rate and reducing information entropy of evaluation index, a mobile e-commerce platform consumer credit evaluation method based on random forest is designed. First, we use the honeycomb algorithm to optimise the size and depth of the decision tree and the capacity of the feature subset of the classification attribute, and use the random forest to mine the classification of consumer data. Then, the evaluation index system is constructed, and the index weight value is calculated according to the information entropy. Finally, establish a comment set and determine the membership degree of the indicators, and combine the weight of the indicators with the judgment matrix to obtain the consumer credit evaluation results. Experiments show that the recall rate of this method is between 83.74%-85.28%, the information entropy is always lower than 0.02, and the maximum AUC value can reach 0.928.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Networking and Virtual Organisations (IJNVO):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook