Relevance vector machine with tuning based on self-adaptive differential evolution approach for predictive modelling of a chemical process

In the past decade, relevance vector machines have gained the attention of many researchers, and this machine learning technique is a Bayesian sparse kernel method, both for classification and regression problems. In general, the choice of appropriate learning hyperparameters is a crucial step in obtaining a well-tuned model. To overcome this issue, we apply a self-adaptive differential evolution algorithm. In this paper, we propose a relevance vector machine for regression combined with a novel self-adaptive differential evolution approach for predictive modelling of phosphorus concentration levels in a steelmaking process with real data. We compared the performance of proposed relevance vector machine (RVM) with other machine learning techniques, such as random forest (RF), artificial neural network (ANN), K-nearest neighbors (K-NN), and also with statistical learning techniques as, Beta regression model and multiple linear regression model. The RVM has performance better than RF, ANN, K-NN, and statistical techniques used. Our study indicates that RVM models are an adequate tool for the prediction of the phosphorus concentration levels in the steelmaking process. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

Relevance vector machines, a Bayesian sparse kernel method, coupled with a self-adaptive differential evolution algorithm, show superior performance in predicting phosphorus concentration levels in the steelmaking process. The study indicates that RVM models are an adequate tool for such predictions.