Outlier Score

Outlier score methods aim to quantify the degree to which data points deviate from a typical distribution, facilitating the identification of anomalies. Current research emphasizes improving the interpretability and comparability of outlier scores across different algorithms, often by transforming them into probabilities or rankings, and enhancing their accuracy, particularly for outliers that are crucial to detect in applications like healthcare and finance. This involves exploring various model architectures, including those based on distance metrics, probabilistic transformations, and ensemble methods like isolation forests, with a focus on improving efficiency and scalability for large datasets. The development of robust and interpretable outlier detection techniques has significant implications for diverse fields, enabling more effective anomaly detection and improved decision-making in various applications.