Discriminant Function Selection in Binary Classification Task

The ensemble selection is one of the important problems in building multiple classifier systems (MCSs). This paper presents dynamic ensemble selection based on the analysis of discriminant functions. The idea of the selection is presented on the basis of binary classification tasks. The paper presents two approaches: one takes into account the normalization of the discrimination functions, and in the second approach, normalization is not performed. The reported results based on the data sets form the UCI repository show that the proposed ensemble selection is a promising method for the development of MCSs.