Oh, Hwan In Jung, Joon Ha Sun, Kyung Ho 2025-01-01 2288-5226 https://aurora.ajou.ac.kr/handle/2018.oak/38174 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105000402849&origin=inward In various industrial sectors, data-driven machine system condition diagnosis is crucial for maintaining machine performance and safety, making it essential to analyze data patterns and accurately understand the state of machines. Scatter plots, which are commonly utilized as visualization techniques, are employed for data pattern analysis. However, in complex systems with numerous features, creating and analyzing scatter plots for all feature pairs becomes impractical. Therefore, this study proposes a method for automating scatter plot creation and feature selection based on Euclidean distance. This approach efficiently identifies critical features in the data analysis process, ensuring consistency and accuracy in variable selection and is expected to contribute to machine system condition diagnosis and performance optimization. kor Korean Society of Mechanical Engineers Automatic feature selection Data patterns Euclidean distance Feature selection algorithm Features selection Imbalanced dataset Machine systems Scatter plots System conditions Two-dimensional Automatic Feature Selection Algorithm Based on Two-Dimensional Scatter Plots for Imbalanced Datasets 불균형 데이터에도 적용가능한 2차원 산점도 기반 특성인자 자동 선택 알고리즘 Article 212 3 201 Transactions of the Korean Society of Mechanical Engineers, A 49 Transactions of the Korean Society of Mechanical Engineers, A, Vol.49 No.3, pp.201-212 10.3795/ksme-a.2025.49.3.201 2-s2.0-105000402849 https://www.dbpia.co.kr/Society/articleDetail/NODE12095227?pubId=10064&selPid=&isView=N Euclidean Distance Feature Selection Scatter Plot Article 12264873 false Mechanical Engineering