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http://dx.doi.org/10.13089/JKIISC.2018.28.6.1499

Black Consumer Detection in E-Commerce Using Filter Method and Classification Algorithms  

Lee, Taekyu (Institute of Cyber Security & Privacy (ICSP), Korea University)
Lee, Kyung Ho (Institute of Cyber Security & Privacy (ICSP), Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.28, no.6, 2018 , pp. 1499-1508 More about this Journal
Abstract
Although fast-growing e-commerce markets gave a lot of companies opportunities to expand their customer bases, it is also the case that there are growing number of cases in which the so-called 'black consumers' cause much damage on many companies. In this study, we will implement and optimize a machine learning model that detects black consumers using customer data from e-commerce store. Using filter method for feature selection and 4 different algorithms for classification, we could get the best-performing machine learning model that detects black consumer with F-measure 0.667 and could also yield improvements in performance which are 11.44% in F-measure, 10.51% in AURC, and 22.87% in TPR.
Keywords
Machine Learning; Supervised Learning; Fraud Detection; User Classification; Feature Selection;
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