Intelligent feature extraction for ensemble of classifiers
Paulo V. W. Radtke, Robert Sabourin, Tong Wong · 2005
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.