Method and system for creating an ensemble of neural network-based classifiers that optimizes a diversity metric

Abstract

One embodiment provides a system which facilitates construction of an ensemble of neural network-based classifiers that optimize a diversity metric. During operation, the system defines a diversity metric based on pairwise angles between decision boundaries of three or more affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing a pair of mutually orthogonal affine classifiers of the three or more affine classifiers. The system trains one or more neural networks such that parameters of the one or more neural networks are consistent with parameters of the affine classifiers to obtain an ensemble of neural network-based classifiers which optimize the diversity metric. The system predicts an outcome for a testing data object based on the obtained ensemble of neural-network based classifiers which optimize the diversity metric.