Data based multivariate pseudo correlation analysis in steel industry for optimized variable selection

Data driven variable selection, without including physical knowledge, is an important prerequisite for many applications in the field of data based modeling. This paper deals with a novel approach to optimize the dimension of the input space by a combination of common variable selection methods with multivariate correlation analysis. The results are input structures with revised pseudo correlations between input channels and a physically better interpretable structure. The presented method is successfully applied to measured data from steel industry. Some exemplary results are shown in this paper.