K-Nearest Oracle for Dynamic Ensemble Selection

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Abstract

For handwritten pattern recognition, multiple classifier system has been shown to be useful in improving recognition rates. One of the most important issues to optimize a multiple classifier system is to select a group of adequate classifiers, known as 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 does not give better performance than static selection. We propose four new dynamic selection schemes which explore the property of the oracle concept. The result suggests that the proposed schemes are apparently better than the static selection using the majority voting rule for combining classifiers.

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