Title: Consumer credit evaluation of mobile e-commerce platform based on random forest

Authors: Ming Yang

Addresses: School of Computing, College of Arts and Sciences, University Utara Malaysia, Malaysia; Business School, Chongqing College of Electronic Engineering, Chongqing 401331, China

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.

Keywords: mobile e-commerce platform; consumer credit; credit evaluation; random forest algorithm; decision tree.

DOI: 10.1504/IJNVO.2023.135952

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.272 - 284

Received: 09 Mar 2023
Accepted: 12 Jun 2023
Published online: 10 Jan 2024 *

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