Classifier Pool Generation based on a Two-level Diversity Approach

• URL: http://arxiv.org/abs/2011.01908v1
• Date: Tue, 3 Nov 2020 18:41:53 GMT
• Title: Classifier Pool Generation based on a Two-level Diversity Approach
• Authors: Marcos Monteiro, Alceu S. Britto Jr, Jean P. Barddal, Luiz S. Oliveira, Robert Sabourin
• Abstract summary: This paper describes a pool generation method guided by the diversity estimated on the data complexity and decisions. The complexity measures with high variability across the subsamples are selected for posterior pool adaptation, where an evolutionary algorithm optimize diversity in both complexity and decision spaces. Results show significant accuracy improvements in 69.4% of the experiments when Dynamic Selection and Dynamic Ensemble Selection methods are applied.
• Score: 14.617208698215808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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