The Online Soft Computing Models of key variables based on the Boundary Forest method

Abstract

The Online Soft Computing Models (OSCMs) based on ensemble methods are novel and quite effective data-driven tools for predicting key variables. The current challenge encountered by them is how to enhance the reliability caused by both the uncertainty from noise and the unsuitable specifications of models, on the premise of high predicting accuracy and low computational cost. To meet the current challenge, the OSCM based on the Boundary Forest (OSCM-BF) is proposed in this paper. The BF combines a set of the Tree-Structure Ensemble (TSE) models. In terms of the different values of θ (i.e., the minimum size of leaf nodes), the BF enhances the reliability of a single TSE not only by overlapping the gap segments of output range (i.e., connecting the discontinuous boundaries of leaf nodes), but also by possessing stronger robustness via producing enough diversity. Moreover, a theoretical range of the value of θ constructed by BF is provided. Since the simplicity, the nice interpretability and the flexibility on large-scale data, the moving-window strategy was adopted to realize the update of the BF models. The experiments on the noisy data from the industrial process of Ladle Furnace reveal that the OSCM-BF can enhance the reliability of the OSCM-TSE on the premise of high predicting accuracy and low computational cost.