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From dynamic classifier selection to dynamic ensemble selection
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DOI：10.1016/j.patcog.2007.10.015.png)
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Abstract
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In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection performs no better than static selection. We propose four new dynamic selection schemes which explore the properties of the oracle concept. Our results suggest that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method. (c) 2007 Elsevier Ltd. All rights reserved.
Keywords:
oracle
combining classifiers
classifier selection
ensemble selection
pattern recognition
majority voting
ensemble of learning machines
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