International Business Machines Corporation
Decision boundary enhancement for learning models

Abstract:

Methods, systems, and computer program products for machine learning model decision boundary enhancement are provided. Aspects include determining a decision boundary associated with a first machine learning model, wherein the first machine learning model is trained with an initial training set of features and associated classifications from a verification model, obtaining a plurality of new feature sets, analyzing, by the first machine learning model, the plurality of new feature sets to determine that a subset of feature sets have classification predictions within a threshold range of the decision boundary associated with the first machine learning model, inputting the subset of feature sets into the verification model to determine enhanced training data based on outputs of the verification model, and creating an enhanced machine learning model by further training the first machine learning model with the initial training data and the enhanced training data.