This paper presents a two-level approach to create ensemble of classifiers based on intelligent feature extraction and multi-objective genetic optimization. The first stage optimizes a set of representations, which is used to create classifiers. The second stage then optimizes the ensemble's aggregated classifiers. To assess the approach's feasibility, a set of tests with isolated handwritten digits is performed. The experimental results encourage further researches in this direction, as the optimized ensemble of classifiers outperforms the single classifier approach.
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