Classifier Pool Generation based on a Two-level Diversity Approach
This work addresses the challenge of improving ensemble performance in machine learning, though it is incremental as it builds on existing diversity and selection techniques.
The paper tackled the problem of generating diverse classifier pools by using a two-level diversity approach based on data complexity and classifier decisions, resulting in significant accuracy improvements in 69.4% of experiments with dynamic selection methods.
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.