Simulation of the ensemble generation process: The divergence between data and model similarity

Abstract

In this paper we present a Netlogo simulation model for a Data Mining methodological process: ensemble classifier generation. The model allows to study the trade-off between data characteristics and diversity, a key concept in Ensemble Learning. We studied the re™ search hypothesis that data characteristics should also be taken into account while generating ensemble classifier models. The results of our experiments indicate that diversity is in fact a key concept in Ensemble Learning but regarding our research hypothesis, the findings axe inconclusive.