K-Nearest Oracle for Dynamic Ensemble Selection
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 mul- tiple classifier system is to select a group of adequate clas- sifiers, 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 bet- ter 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 major- ity voting rule for combining classifiers.
Citation:
A.H.-R. Ko, R. Sabourin, A. Britto Jr., "K-Nearest Oracle for Dynamic Ensemble Selection," icdar,pp.422-426, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 1, 2007