Effectiveness of tree-based ensembles for anomaly discovery: insights, batch and streaming active learning

HIGHLIGHTS

• What: The authors provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. The authors develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. To handle streaming data setting, the authors develop a novel algorithm to robustly detect drift in data streams and design associated algorithms to adapt the anomaly detector on-the-fly in a principled manner.. The overall goal is to minimize the number of . . .
  

   

  

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