USVDD: A one-class outlier detection method with imprecisely-observed data

doi:10.1016/j.asoc.2025.113065

USVDD: A one-class outlier detection method with imprecisely-observed data

2025-05-01

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Jingyu Liang

刘洁 (Jie Liu) *

DOI：10.1016/j.asoc.2025.113065

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Detecting outliers is essential to recognizing potential faults in high-value equipment. Since such equipment typically undergoes careful maintenance to prevent breakdowns, outlier detection often involves a one-class classification problem. Current advanced methods frequently face difficulties in managing the complexities of one-class observations, particularly when dealing with uncertain and imprecise monitoring data. This study proposes a novel approach to fault detection, called uncertain support vector data description (USVDD), in contexts involving one-class uncertain data. Drawing on uncertainty theory, USVDD conceptualizes imprecise observations as uncertain variables. By integrating novel hyperparameters, particularly belief degrees alpha k, the method effectively addresses and quantifies data uncertainty, enhancing its ability to handle complex, uncertain datasets. Testing on 12 real-world datasets highlights that the proposed USVDD method outperforms alternative methods by achieving the highest balanced F1-scores on 10 datasets. Additionally, it demonstrates remarkable efficiency, completing computations on high-dimensional datasets up to 60 times faster than competing methods on the same hardware setup. USVDD excels in managing observational data influenced by aleatory uncertainty, making it a reliable solution for one-class diagnostic modeling in situations with scarce fault samples.

Keywords:

Fault detection

One-class classification

Imprecise data with uncertainty

Uncertain support vector data description

USVDD: A one-class outlier detection method with imprecisely-observed data

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