Skip to Main Content
As the number of feature increases, classification accuracy may decrease. Additionally, computational overload increases with a large number of features. For effective classification performance and shortened the training time, the redundant features should be eliminated before the classification process. In this paper, a new HDMR-based feature selection approach is presented, sorting the features with respect to their sensitivity coefficient calculated by HDMR sensitivity analysis. With the experiments conducted, the HDMR-based feature selection approach is competitive with sequential forward feature selection method and faster in terms of computational time, especially when dealing with datasets having a large number of features.