An error correction prediction model based on three-way decision and ensemble learning

As a hot topic in machine learning, prediction has attracted a lot of attention nowadays. Scientific prediction can provide a guide for reducing decision-making losses and making reasonable decisions. However, most of existing prediction models still suffer from limited performance, which cannot reasonably handle complex prediction problems. In addition, there are certain limitations in the scope of different prediction models. In light of the above limitations, the paper proposes a novel error correction prediction model based on the idea of three-way decision (TWD), which is titled an ECP-TWD model. First, the back propagation algorithm optimized neural network (BPNN) model is used to achieve the pre-prediction and obtain initial prediction error series. Second, we further combine the strengths of TWD with ensemble learning, tri-divide all alternatives according to the magnitude of the prediction error of the BPNN model, and apply different strategies to re-predict the prediction error sequence in each region, so as to achieve the correction of predicted values of the BPNN model. Finally, the validity, stability and superiority of the presented model are verified based on the case analysis and experimental analysis. The results show that the ECP-TWD model has the better prediction performance compared to other state-of-the-art prediction models.