Fortinet, Inc.
INDUCTIVE LEARNING AND INDUCTIVE FORGETTING FOR BOLSTERING MACHINE-LEARNING MODEL PERFORMANCE

Abstract:

Systems and methods are described for training a machine learning model using intelligently selected multiclass vectors. According to an embodiment, an un-labeled feature vector is selected from a set of feature vectors. A model classified cluster and a confidence score are obtained by classifying an un-labeled feature vector using a machine-learning model. A determination is made regarding whether the confidence score is greater than a threshold. When the determination is affirmative: (i) for each labeled feature vector, determining a distance metric for the un-labeled feature vector with respect to the labeled feature; (ii) determining a statistically matching cluster of labeled feature vectors to which the un-labeled feature vector is closest; and (iii) when the model classified cluster and the statistically matching cluster are one and the same: (a) labeling the un-labeled feature vector; and (b) model fitting the machine learning model based on the labeling.