Outlier Data

Outlier data detection aims to identify data points significantly deviating from the norm within a dataset, a crucial task across diverse fields like finance, healthcare, and cybersecurity. Current research emphasizes developing robust and efficient outlier detection methods, focusing on techniques like Isolation Forest, transformer-based foundation models, and graph neural networks, often addressing challenges related to model selection, limited outlier data, and interpretability of results. These advancements improve the accuracy and reliability of outlier detection, leading to more trustworthy models and enabling better decision-making in various applications by mitigating the impact of erroneous or anomalous data.