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• W3138399413 endingPage "100150" @default.
• W3138399413 startingPage "100150" @default.
• W3138399413 abstract "Semi-supervised data are quite common in the process industry, which has caught much attention in recent years. The semi-supervised feature of process data not only has a great impact on data mining and analytics, but also matters in feature extraction and knowledge discovery in the process. In this paper, the framework of semi-supervised data modeling and applications is formulated for the process industry. First, the semi-supervised data structure is introduced, including the causes of semi-supervised data structure, the main feature of the semi-supervised data, and its effects on data modeling and applications in the process industry. Second, detailed research statuses on semi-supervised data modeling and applications in the process industry are illustrated, with introductions of some representative approaches. Third, several challenges and promising issues on modeling and application of semi-supervised data are discussed and highlighted for future research." @default.
• W3138399413 created "2021-03-29" @default.
• W3138399413 creator A5067726465 @default.
• W3138399413 date "2021-06-01" @default.
• W3138399413 modified "2023-10-17" @default.
• W3138399413 title "Semi-supervised data modeling and analytics in the process industry: Current research status and challenges" @default.
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