Description: Judgment-based anomaly detection is an approach that relies on the expertise and knowledge of specialists to identify irregularities in datasets. This method is distinguished by its ability to interpret complex patterns and specific contexts that may not be easily detectable by automated algorithms. Experts often use their intuition and domain understanding to highlight data that deviates from the expected, which can be crucial in fields where variability is high and data is noisy. This approach is particularly valuable in situations where the amount of data is limited or where a deep understanding of underlying dynamics is required. Judgment-based anomaly detection not only focuses on identifying outliers but also considers the context and relationships between different variables, allowing for a more nuanced and accurate assessment of the data. In an increasingly AI-driven world, this human approach complements automated techniques, providing a critical perspective that can enhance the accuracy and relevance of findings.
