Using good and bad diversity measures in the design of ensemble systems: A genetic algorithm approach

Abstract

This paper investigates the influence of measures of good and bad diversity when used explicitly to guide the search of a genetic algorithm to design ensemble systems. We then analyze what the best set of objectives between classification error, good diversity and bad diversity as well as all combination of them. In this analysis, we make use of the NSGA II algorithm in order to generate ensemble systems, using k-NN as individual classifiers and majority vote as the combination method. The main goal of this investigation is to determine which set of objectives generates more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversity) have a positive effect in the construction of ensembles and if they can replace the classification error as optimization objective without causing losses in the accuracy level of the generated ensembles.