Linear Priors Mined and Integrated for Transparency of Blast Furnace Black-Box SVM Model

Shaohan Chen, Chuanhou Gao

Abstract: Black-box models are a kind of effective means to describe extremely complex systems, such as blast furnaces. However, an evident deficiency for them lies in the lack of comprehensibility and transparency. For this reason, the current work, starting with the black-box model input port, contributes to enhancing the transparency of the blast furnace soft-margin support vector machine (SVM) model. In this article, we first develop a novel algorithm to mine linear prior knowledge from data sets. Then, the mined priors are integrated into the black-box soft-margin SVM model in the form of inequality constraints to create a partly transparent soft-margin SVM (pTsm-SVM) optimization model. The pTsm-SVM model has the advantages of both black-box models and white-box models, i.e., high precision and some transparency. Finally, we exhibit the effectiveness of the pTsm-SVM model through two real blast furnace examples.