Abstract
This paper introduces a new classification method that uses the differential evolution algorithm to feature-wise select, from a pool of distance measures, an optimal distance measure to be used for classification of elements. The distances yielded for each feature by the optimized distance measures are aggregated into an overall distance vector for each element by using OWA based multi-distance aggregation.
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Koloseni, D., Fedrizzi, M., Luukka, P., Lampinen, J., Collan, M. (2015). Differential Evolution Classifier with Optimized OWA-Based Multi-distance Measures for the Features in the Data Sets. In: Angelov, P., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-11313-5_67
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DOI: https://doi.org/10.1007/978-3-319-11313-5_67
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11312-8
Online ISBN: 978-3-319-11313-5
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