Fortinet, Inc.
INTELLIGENT VECTOR SELECTION BY IDENTIFYING HIGH MACHINE-LEARNING MODEL SKEPTICISM

Abstract:

Systems and methods are described for training a machine learning model using intelligently selected multiclass vectors. According to an embodiment, a processing resource of a computer system receives a set of feature vectors. For each feature vector of the set of feature vectors: (i) the feature vector is classified as one of multiple classes using a machine-learning model trained for multiclass classification; and (ii) a prediction skepticism metric, representing a degree of prediction skepticism relating to classification of the feature vector by the machine-learning model, is calculated for the feature vector using a heuristic function. A boundary condition vector is selected from the set of feature vectors for labeling having a highest degree of prediction skepticism.