Learning-Based Confidence Estimation for Multi-modal Classifier Fusion

摘要

We propose a novel confidence estimation method for predictions from a multi-class classifier. Unlike existing methods, we learn a confidence-estimator on the basis of a held-out set from the training data. The predicted confidence values by the proposed system are used to improve the accuracy of multi-modal emotion and sentiment classification. The scores of different classes from the individual modalities are superposed on the basis of confidence values. Experimental results demonstrate that the accuracy of the proposed confidence based fusion method is significantly superior to that of the classifier trained on any modality separately, and achieves superior performance compared to other fusion methods.