Description: Utility-Based Anomaly Detection is an innovative approach that evaluates the relevance and impact of identifying anomalies in a dataset. Unlike traditional methods that focus solely on identifying outlier data points, this approach considers the practical utility of detecting such anomalies, i.e., how their identification can influence decision-making and process improvement. This method is based on the premise that not all anomalies are equally important; some may have a significant impact on system performance, while others may be irrelevant or even detrimental if given too much attention. Therefore, Utility-Based Anomaly Detection seeks to optimize the detection process by prioritizing those anomalies that offer the greatest value in terms of information and action. This approach is particularly relevant in contexts where resources are limited and efficient information management is required, allowing organizations to focus on the anomalies that can truly influence their strategic objectives.
