The product quality risk assessment of e-commerce by machine learning algorithm on spark in big data environment

Issue title: Special Section: Fuzzy theoretical model analysis for signal processing

Guest editors: Valentina E. Balas, Jer Lang Hong, Jason Gu and Tsung-Chih Lin

Article type: Research Article

Authors: Liu, Yi^{a; b; *} | Lu, Jiahuan^a | Mao, Feng^a | Tong, Kaidi^a

Affiliations: [a] Management School, Hangzhou Dianzi University, Hangzhou, China | [b] Zhejing Institution of Informatization Development, Hangzhou, China

Correspondence: [*] Corresponding author. Yi Liu, Management School, Hang-zhou Dianzi University, Hangzhou, China. E-mail: [email protected].

Abstract: In order to pre-warning the product quality risk of the e-commerce platform, this paper studies the machine learning algorithm for the products quality risk assessment, which propose the Fuzzy C-Means clustering algorithm for the feature extraction and the Cost Sensitive Leaning (CSL)-Naive Bayesian algorithm to construct the assessment model for E-commerce product quality risk form the massive and unbalanced data. The experimental results show that the Machine Learning algorithm based on Spark has better scalability and superiority in the large-scale data environment, which can accurately identify e-commerce product quality risk.

Keywords: E-commerce product quality, risk assessment, fuzzy c-means clustering algorithm, cost sensitive leaning, Naive Bayesian algorithm

DOI: 10.3233/JIFS-179305

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 4705-4715, 2019