The capability to perform anomaly detection in a resource-constrained setting, such as an edge device or a loaded server, is of increasing need due to emerging on-premises computation constraints as well as security, privacy and profitability reasons. Yet, the increasing size of datasets often results in current anomaly detection methods being too resource consuming, and in particular decision-tree based ensemble classifiers. To address this need, we present RADE—a new resource-efficient anomaly detection framework that augments standard decision-tree based ensemble classifiers to perform well in a resource constrained setting. The key idea behind RADE is first to train a small model that is sufficient to correctly classify the majority of the queries. Then, using only subsets of the training data, train expert models for these fewer harder cases where the small model is at high risk of making a classification mistake. We implement RADE as a scikit-learn classifier. Our evaluation indicates that RADE offers competitive anomaly detection capabilities as compared to standard methods while significantly improving memory footprint by up to 12×, training-time by up to 20×, and classification time by up to 16×.




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Research Areas

  • Anomaly Detection
  • Machine Learning




Machine Learning