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SYSTEMS AND METHOD FOR ENHANCED ACTIVE MACHINE LEARNING THROUGH PROCESSING OF PARTITIONED UNCERTAINTY

Abstract:

Systems and methods are disclosed herein for improving machine learning of a data set. In one example, the method may include training a predictive model on an initial data set comprising labeled data, wherein the training is performed in an active learning system. The method may further include generating a set of parameters based on the training and introducing an unlabeled data set into the predictive model. According to some embodiments, the method may further include applying the set of parameters to the unlabeled data set, generating a set of predictions associated with the applied set of parameters and calculating a first uncertainty score and a second uncertainty score associated with the generated set of predictions. Moreover, the method may also include modifying the data set based on the first uncertainty score, and modifying the predictive model based on the second uncertainty score.