Learning Emotion Assessment Method Based on Belief Rule Base and Evidential Reasoning

Learning emotion assessment is a non-negligible step in analyzing learners’ cognitive processing. Data are the basis of learning assessment. However, existing models cannot balance model accuracy and interpretability well due to influence uncertainty process data collection parameter errors. Given above problems, new based on evidence reasoning belief rule base (E-BRB) proposed this paper. First, transformation matrix introduced transform multiple emotional indicators into same standard framework integrate them, which keeps consistency information transformation. Second, relationship between states modeled by E-BRB conjunction with expert knowledge. In addition, we employ projection covariance adaptation evolution strategy (P-CMA-ES) optimize parameters improve model’s accuracy. Finally, demonstrate effectiveness model, it applied science learning. The experimental results show that has better than data-driven such as neural networks.