Computer Information Systems and Industrial Management

Statistically, since Pearson, data are recorded as matrices of size n p, where rows contain n subjects (individuals, cases), and columns are values of the p variables (attributes) characterizing the subjects. When performing traditional multivariate analysis of the recorded data, the crucial problem is: should all the p recorded variables be taken for the analysis; may be less of them will be sufficient and some of them are not relevant, or even an impediment. The old saying: “the more the better” has become questionable nowadays: too many non-relevant variables may be disturbing by introducing some random effects into the data. The problem to solve is composite. I will consider it in the context of regression or classification analysis, when dealing with directly recorded ‘variables’ (no ‘features’ derived from them). I will concentrate on group of methods referred to as Collective Intelligence (contains, among others, Ensemble Learning, Decision trees and Random Forests). Specifically, I will concentrate on the Random Forests (RFs) methodology. RFs offer some non-conventional indices of importance of variables in the context of regression and clustering. They work directly on original variables (not on new features derived from them). They can work on mixed type variables, that is quantitative (numeric) or qualitative (categorial). They work without assumption on the probability distribution of the variables. They yield an internal unbiased estimate of the generalization error. It has been shown that RFs are resistant to outliers, however not all of them are universally consistent. I intend to show – on real data examples – how all this works in practice. A Data-Driven Approach Towards Forecasting Generalized Mid-Term Energy Requirement for Industrial Sector Users of Smart Grid