Diversity versus quality in classification ensembles based on feature selection

Abstract

Feature subset-selection has emerged as a useful technique for creating diversity in ensembles - particularly in classification ensembles. In this paper we argue that this diversity needs to be monitored in the creation of the ensemble. We propose an entropy measure of the outputs of the ensemble members as a useful measure of the ensemble diversity. Further, we show that using the associated conditional entropy as a loss function (error measure) works well and the entropy in the ensemble predicts well the reduction in error due to the ensemble. These measures are evaluated on a medical prediction problem and are shown to predict the performance of the ensemble well. We also show that the entropy measure of diversity has the added advantage that it seems to model the change in diversity with the size of the ensemble.