Open AccessA Data Mining System for Potential Customers Based on One-Class Support Vector Machine
Author(s) -
Weijian Mai,
Fengjie Wu,
Fang Li,
Wenjun Luo,
Xiaoting Mai
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2031/1/012066
Subject(s) - support vector machine , class (philosophy) , novelty , computer science , novelty detection , binary classification , data mining , commodity , focus (optics) , machine learning , artificial intelligence , binary number , structured support vector machine , business , mathematics , finance , philosophy , physics , theology , arithmetic , optics
Commodity purchase data is usually severely skewed, which is reflected in the fact that there are far more negative data than positive data. This phenomenon makes it difficult for the binary classification model to obtain satisfactory results. Hence, we transform the binary classification problem into a one-class novelty detection problem. Specifically, this work proposes a potential customer mining system based on the One-class Support Vector Machine (OCSVM) and demonstrates its effectiveness for classification, prediction, and potential customer mining. This system allows merchants to focus on unpurchased customers with the strongest purchase intentions and to change their purchase decisions with minimal sale costs, which enables merchants to maximize their benefits.