outlier ensembles

Outlier Ensembles is an ensemble learning method that improves the accuracy and robustness of anomaly detection by combining the results of multiple anomaly detection algorithms. Its goal is to identify observations in the dataset that deviate from normal patterns, which may indicate potential errors or significant events. Outlier Ensembles have wide application value in areas such as financial fraud detection, cybersecurity, and medical diagnosis, effectively enhancing the system's anomaly detection capabilities.