Multi-Classification using One-versus-One Deep Learning Strategy with
  Joint Probability Estimates
Anthony Hei-Long Chan Raymond HonFu Chan Lingjia Dai
The One-versus-One (OvO) strategy is an approach of multi-classification
models which focuses on training binary classifiers between each pair of
classes. While the OvO strategy takes advantage of balanced training data, the
classification accuracy is usually hindered by the voting mechanism to combine
all binary classifiers. In this paper, a novel OvO multi-classification model
incorporating a joint probability measure is proposed under the deep learning
framework. In the proposed model, a two-stage algorithm is developed to
estimate the class probability from the pairwise binary classifiers. Given the
binary classifiers, the pairwise probability estimate is calibrated by a
distance measure on the separating feature hyperplane. From that, the class
probability of the subject is estimated by solving a joint probability-based
distance minimization problem. Numerical experiments in different applications
show that the proposed model achieves generally higher classification accuracy
than other state-of-the-art models.