  This paper describes a classifier pool generation method guided by the
diversity estimated on the data complexity and classifier decisions. First, the
behavior of complexity measures is assessed by considering several subsamples
of the dataset. The complexity measures with high variability across the
subsamples are selected for posterior pool adaptation, where an evolutionary
algorithm optimizes diversity in both complexity and decision spaces. A robust
experimental protocol with 28 datasets and 20 replications is used to evaluate
the proposed method. Results show significant accuracy improvements in 69.4% of
the experiments when Dynamic Classifier Selection and Dynamic Ensemble
Selection methods are applied.
