D. Opitz & J. Shavlik (1996).
Actively Searching for an Effective Neural-Network Ensemble. Connection Science, 8, pp. 337-353.
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A neural-network ensemble is a very successful technique where the outputs of a set of separately trained neural network are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm called ADDEMUP that uses genetic algorithms to explicitly search for a highly diverse set of accurate trained networks. ADDEMUP works by first creating an initial population, then uses genetic operators to continually create new networks, keeping the set of networks that are highly accurate while disagreeing with each other as much as possible. Experiments on four real-world domains show that ADDEMUP is able to generate a set of trained networks that is more accurate than several existing ensemble approaches. Experiments also show that ADDEMUP is able to effectively incorporate prior knowledge, if available, to improve the quality of its ensemble.
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