Two stage ensemble selection algorithm

Multiple Classifiers Systems (MCSs) very often improve the accuracy of classification when compared with base classifiers. The building of MCSs consists of three phases: generation, selection and integration. The paper presents the two stage dynamic ensemble selection based on the analysis of the discriminant functions. The proposed in the work algorithm is applied to the binary classification tasks. In the integration phase we use the sum rule. Reported results based on the ”Pima” data set show that the proposed two stage ensemble selection is a promising method for the development of MCSs.