FEEDBACK-BASED TRAINING FOR ANOMALY DETECTION

• Main Authors: BHOTIKA, Rahul, BALASUBRAMANIAN, Barath, BROUWERS, Niels, PATEL, Anant, NAMBIAR, Rakesh Madhavan, AGHORAM RAVICHANDRAN, Avinash, SWAMINATHAN, Gurumurthy, KRISHNAN, Prakash, MAINTHIA, Anushri, DAS, Ranju, MCDOWELL, Shaun Ryan James, ZEPEDA SALVATIERRA, Joaquin
• Format: Patent
• Language: English
• Published: 02.06.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; HANDLING RECORD CARRIERS; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

Techniques for feedback-based training are described. An exemplary method includes receiving a request to perform feedback-based retraining, the request including one or more of an identifier of one or more models to retrain, an identifier of a dataset to use for retraining, an identifier of a dataset to use for testing, an indication of a threshold for an anomaly, an indication of how to display items to verify, and an indication of where to store historical information; applying the selected scoring machine learning model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, at least one of a score and a confidence for the score; providing a result of the application of the selected scoring machine learning model on an unlabeled dataset to request feedback in the form of a graphical user interface; receiving the requested feedback via the graphical user interface; adding data from the unlabeled dataset into the training dataset when the received requested feedback indicates a verified result; and retraining the selected scoring machine learning model using the training data with the added data from the unlabeled dataset.