SEPARATION MAXIMIZATION TECHNIQUE FOR ANOMALY SCORES TO COMPARE ANOMALY DETECTION MODELS

• Main Authors: Agarwal, Nipun, Vasic, Milos, Agrawal, Sandeep, Varadarajan, Venkatanathan, Fathi Moghadam, Hesam, Yakovlev, Anatoly, Jinturkar, Sanjay, Casserini, Matteo, Hopkins, Robert
• Format: Patent
• Language: English
• Published: 05.05.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

In an embodiment based on computer(s), an ML model is trained to detect outliers. The ML model calculates anomaly scores that include a respective anomaly score for each item in a validation dataset. The anomaly scores are automatically organized by sorting and/or clustering. Based on the organized anomaly scores, a separation is measured that indicates fitness of the ML model. In an embodiment, a computer performs two-clustering of anomaly scores into a first organization that consists of a first normal cluster of anomaly scores and a first anomaly cluster of anomaly scores. The computer performs three-clustering of the same anomaly scores into a second organization that consists of a second normal cluster of anomaly scores, a second anomaly cluster of anomaly scores, and a middle cluster of anomaly scores. A distribution difference between the first organization and the second organization is measured. An ML model is processed based on the distribution difference.