Machine learning classifying of data using decision boundaries

Abstract

Classifier accuracy is increased in machine learning applications by training a machine learning (ML) model including a classifier across classes by determining weighted input points for a contributing set to store the values for true positive and true negative predications. In a following step, input data is provided to the classifier of the machine learning model at runtime, and a classification output is determined from the classifier. For the classification output, values for input during runtime are compared with a sample of inputs stored for training the machine learning model to determine the distance in spread for the classification output. A class is determined from the classification output having a smallest distance and spread. The method can further determine if the class with the smallest distance and spread is a true positive or true negative by comparing the class with the smallest distance and spread with the classification output.