CSLSEP: an ensemble pruning algorithm based on clustering soft label and sorting for facial expression recognition

Applying ensemble learning to facial expression recognition is an important research field nowadays, but all may not be better than many, the redundant learners in the classifier pool may hinder the ensemble system’s performance, so ensemble pruning is needed. Ensemble pruning selects the most suitable subset of classifiers to classify test samples according to the classifier competence. However, the noisy and redundant samples in the validation set will often adversely affect the evaluation of the classifier, making it impossible to select the most suitable classifier. In this paper, a novel ensemble pruning algorithm based on clustering soft label optimization and sorting for facial expression recognition is proposed. First, to increase classifier evaluation objectivity, the novel method uses the clustering optimization model to perform prototype selection and classifier clustering simultaneously. Then the accuracy-based ordering is employed to remove the redundant or poor quality learners, and keep a balance between diversity and accuracy of the ensemble system. Experimental results show that the proposed method outperforms or competes with some state-of-the-art methods on several typical facial expression datasets.