N-1 Experts: Unsupervised Anomaly Detection Model Selection
Constantin Le Clei, Yasha Pushak, Fatjon Zogaj, Moein Owhadi Kareshk, Zahra Zohrevand, Robert Harlow, Hesam Fathi Moghadam, Sungpack Hong, Hassan Chafi
25 July 2022
Manually finding the best combination of machine learning training algorithm, model and hyper-parameters can be challenging. In supervised settings, this burden has been alleviated with the introduction of automated machine learning (AutoML) methods. However, similar methods are noticeably absent for fully unsupervised applications, such as anomaly detection. We introduce one of the first such methods, N-1 Experts, which we compare to a recent state-of-the-art baseline, MetaOD, and show favourable performance.
Venue : The First International AutoML Conference Late-Breaking Workshop