A Mallows-like criterion for anomaly detection with random forest implementation

Anomaly detection plays a crucial role in fields such as information security and industrial production. It relies on the identification of rare instances that deviate significantly from expected patterns. Reliance on a single model can introduce uncertainty, as it may not adequately capture the complexity and variability inherent in real-world datasets. Under the framework of model averaging, this paper proposes a criterion for the selection of weights in the aggregation of multiple models, employing a focal loss function with Mallows' form to assign weights to the base models. This strategy is integrated into a random forest algorithm by replacing the conventional voting method. Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. These results highlight a notable enhancement in both accuracy and robustness, indicating that model averaging methods can effectively mitigate the challenges posed by data imbalance.