Dynamic Ensemble Selection – Application to Classification of Cutting Tools

Abstract

In order to improve pattern recognition performance of an individual classifier an ensemble of classifiers is used. One of the phases of creating the multiple classifier system is the selection of base classifiers which are used as the original set of classifiers. In this paper we propose the algorithm of the dynamic ensemble selection that uses median and quartile of correctly classified objects. The resulting values are used to define the decision schemes, which are used in the selection of the base classifiers process. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The obtained results clearly indicate that the proposed algorithm improves the classification measure. The improvement concerns the comparison with the ensemble of classifiers method without the selection.