A three-way decision-based ensemble pruning algorithm for facial expression recognition

Abstract:

By removing weak and redundant learners, ensemble pruning can significantly enhance the efficacy of ensemble system-based facial expression recognition. However, existing methods primarily focus on either accepting or rejecting classifiers, which results in the retention of weak classifiers or the exclusion of pivotal ones when evaluation information is unreliable or incomplete. Additionally, relying on accuracy or diversity to evaluate the merits of the classifier is difficult to fully reflect the true performance of the classifier. Consequently, this paper proposed a three-way decision-based ensemble pruning algorithm (3WDEP) for facial expression recognition, which introduced a delayed acceptance strategy to address uncertainties in classifier assessment. Simultaneously, the concept of “predictive preference” was introduced, integrating the correlation measurement between prediction results and actual labels, as well as accuracy and recall metrics, so as to construct an ensemble pruning information system and comprehensively evaluate the classifier performance. The entropy weight method was used to determine the weight of the indicators, and combined with a three-way decision, the loss of classifiers under different decision options was considered to select the classifiers that contributed the most to the ensemble system for integration. Recall was utilized as both a benefit attribute and a cost attribute to optimize the ensemble pruning effect. Experimental results show that 3WDEP effectively improves facial expression recognition performance, and the accuracy improves by 3.32%, 9.39%, 1.26%, and 4.9% compared to the initial ensemble system on FER2013, JAFFE, CK+, and KDEF, respectively.