A geometric approach to feature ranking based upon results of effective decision boundary feature matrix

new method of Feature Ranking (FR) that calculates the relative weight of features in their original domain with an algorithmic procedure. The method supports information selection of real world features and is useful when the number of features has costs implications. The Feature Extraction (FE) techniques, although accurate, provide the weights of artificial features whereas it is important to weight the real features to have readable models. The accuracy of the ranking is also an important aspect; the heuristics methods, another major family of ranking methods based on generate-and-test procedures, are by definition approximate although they produce readable models. The ranking method proposed here combines the advantages of older methods, it has at its core a feature extraction technique based on Effective Decision Boundary Feature Matrix (EDBFM), which is extended to calculate the total weight of the real features through a procedure geometrically justified. The modular design of the new method allows to include any FE technique referable to the EDBFM model; a thorough benchmarking of the various solutions has been conducted.