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. ½ Ò , where is the feature vector extracted from Þ . has the irrelevant features elim-
Radtke, et al. REF presented a two-level approach to create ensemble of classifiers based on intelligent feature extraction and MOGA for recognizing isolated handwritten symbols.
Intelligent feature extraction for ensemble of classifiers
Eighth International Conference on Document Analysis and Recognition (ICDAR'05)
Eighth International Conference on Document Analysis and Recognition (ICDAR'05)
Computer Science