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    This study introduces a novel semi-supervised anomaly detection (AD) method that effectively utilizes limited labeled anomalous data. The approach generates intermediate samples to improve detection performance in complex scenarios.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Anomaly detection (AD) is often treated as an unsupervised task, lacking effective methods when limited labeled anomalous data is available.
    • Existing semi-supervised AD methods inadequately leverage scarce anomalous samples, diminishing their impact on detection.
    • Real-world applications like fault diagnosis and disease detection frequently present scenarios with some labeled anomalies.

    Purpose of the Study:

    • To develop a novel semi-supervised anomaly detection (SAD) method that maximizes the utility of limited labeled anomalous data.
    • To enhance the performance of anomaly detection by effectively integrating scarce anomalous samples into the learning process.
    • To address the limitations of current SAD techniques in fully utilizing available expert-labeled anomalous data.

    Main Methods:

    • A novel SAD method is proposed, learning a nonlinear transformation to map normal and anomalous data into distinct, non-overlapping target distributions.
    • To overcome the scarcity of anomalous samples, intermediate samples are generated through interpolation between normal and anomalous data.
    • These intermediate samples are projected into a third target distribution situated between the normal and anomalous distributions.

    Main Results:

    • The proposed SAD method demonstrates superior performance compared to existing supervised and semi-supervised AD methods.
    • Empirical results across multiple benchmarks and diverse domains validate the effectiveness of the approach.
    • The method successfully boosts detection performance by fully utilizing limited labeled anomalous data.

    Conclusions:

    • The developed SAD method offers a significant advancement in anomaly detection by effectively handling limited labeled anomalous data.
    • The strategy of generating and projecting intermediate samples is crucial for improving SAD performance.
    • This research provides a more robust and effective solution for anomaly detection in scenarios with scarce labeled anomalous data.