Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

23 Jan 2019Shubhomoy Das • Md Rakibul Islam • Nitthilan Kannappan Jayakodi • Janardhan Rao Doppa

In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered. Second, we present several algorithms for active learning with tree-based AD ensembles. Fourth, we present extensive experiments to evaluate our insights and algorithms.

Full paper

Evaluation


No evaluation results yet. Help compare this paper to other papers by submitting the tasks and evaluation metrics from the paper.