Abstract
Mixture of Experts constructing (MxE) is visualized from two slopes: considering diversity in the original training set or diversity in each classifier. Traditionally, the label of the test patterns has been determined by means of an individual classifier, nevertheless another non-traditional methodology of classification will be presented in this work (Mixture of Experts based in Evolutionary Algorithms), with this methodology is possible to guarantee diversity in each member of the MxE. The rules of apprenticeship considered for the MxE are: the Nearest Neighbor Rule and a Modular Neuronal Network. The experiments were obtained using real data bases form the UCI repository.
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Cleofas, L., Valdovinos, R.M., Juárez, C. (2009). Mixture of Experts with Genetic Algorithms. In: Yu, W., Sanchez, E.N. (eds) Advances in Computational Intelligence. Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03156-4_33
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DOI: https://doi.org/10.1007/978-3-642-03156-4_33
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