Multi-Classification using One-versus-One Deep Learning Strategy with Joint Probability Estimates

TL;DR

This paper introduces a novel deep learning-based multi-classification method using a one-versus-one strategy with joint probability estimates, improving accuracy over existing models by calibrating pairwise probabilities and solving a joint probability minimization problem.

Contribution

It proposes a new OvO multi-classification model that incorporates joint probability measures and a two-stage probability estimation algorithm within deep learning.

Findings

Achieves higher classification accuracy than state-of-the-art models.

Effectively calibrates pairwise binary classifier probabilities.

Demonstrates robustness across different application datasets.

Abstract

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.