Diversified Kernel Latent Variable Space and Multi-Objective Optimization for Selective Ensemble Learning-Based Soft Sensor

The improvement of data-driven soft sensor modeling methods and techniques for the industrial process has strongly promoted the development of the intelligent process industry. Among them, ensemble learning is an excellent modeling framework. Accuracy and diversity are two key factors that run through the entire stage of building an ensemble learning-based soft sensor. Existing base model generating methods or ensemble pruning methods always consider the two factors separately, which has limited the development of high-performance but low-complexity soft sensors. To work out this issue, a selective ensemble learning-based soft sensor modeling method based on multi-kernel latent variable space and evolutionary multi-objective optimization is proposed, referred to as MOSE-MLV-VSPLS. This method designs a multiple diversity enhancement mechanism in the base model generation stage. Diversified input variable subspaces are first constructed using the maximum information coefficient on the bootstrapping random resampling subset. Then a set of base models that combine accuracy and diversity are generated on supervised latent variable subspaces under multiple kernel function perturbations. Further, two quantifiable parameters are designed for accuracy and diversity, and the multi-objective gray wolf optimization algorithm is used to select the base models that maximize these two important parameters to achieve effective ensemble pruning at the model ensemble stage. The MOSE-MLV-VSPLS method is applied to two typical industry processes, and the experimental results show that the method is effective and superior in selective ensemble-based soft sensor modeling.