Abstract
This paper presents an approach for building a multi-classifier system in Mean Field Genetic Algorithm (MGA) based inductive learning environments. Several base classifiers are combined with a meta-classifier that learns the bias of base classifiers so that it can draw a decision by combining predictions made by base classifiers. MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). The proposed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA.
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© 2012 Springer-Verlag Berlin Heidelberg
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Kim, Y., Hong, C. (2012). A Multi-classifier System Using Mean Field Genetic Algorithm. In: Lee, G., Howard, D., Ślęzak, D., Hong, Y.S. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Communications in Computer and Information Science, vol 310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32692-9_16
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DOI: https://doi.org/10.1007/978-3-642-32692-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32691-2
Online ISBN: 978-3-642-32692-9
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