Filter Method와 Classification 알고리즘을 이용한 전자상거래 블랙컨슈머 탐지에 대한 연구
Black Consumer Detection in E-Commerce Using Filter Method and Classification Algorithms
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초록
빠른 속도로 성장하고 있는 전자상거래 시장이 기업들에게 고객층을 넓혀나갈 좋은 기회를 제공하고 있는 반면에블랙컨슈머로 인한 기업들의 피해 사례 또한 늘어나고 있다. 본 연구는 전자상거래 고객 데이터를 통해 전자상거래상의 블랙컨슈머를 탐지해내는 머신 러닝 모델을 구축하고 최적화하는 것을 목표로 한다. Feature selection의filter method와 4개의 classification 알고리즘을 이용한 실험을 통해 F-measure 0.667의 정확도로 블랙컨슈머를 탐지하는 모델을 구축하였으며 F-measure에서 11.44%, AURC에서 10.51%, TPR에서 22.87%의 성능향상을 확인 할 수 있었다.
Abstract
Although fast-growing e-commerce markets gave a lot of companies opportunities to expand their customer bases, it isalso the case that there are growing number of cases in which the so-called ‘black consumers’ cause much damage onmany companies. In this study, we will implement and optimize a machine learning model that detects black consumersusing customer data from e-commerce store. Using filter method for feature selection and 4 different algorithms forclassification, we could get the best-performing machine learning model that detects black consumer with F-measure 0.667and could also yield improvements in performance which are 11.44% in F-measure, 10.51% in AURC, and 22.87% in TPR.
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