Abstract
In this paper, a LVQ neural network with a modular approach is presented for the classification of arrhythmias. This new model partitions the dataset into different percentages of the training - test records. In previous research, static (fixed) percentages of training – test dataset were handled, 70% and 30% respectively, however the aim of this research is to approximate the minimum value of records with which the LVQ network can train and classify with a good percentage of accuracy.
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Amezcua, J., Melin, P., Castillo, O. (2015). Design of an Optimal Modular LVQ Network for Classification of Arrhythmias Based on a Variable Training-Test Datasets Strategy. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_32
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DOI: https://doi.org/10.1007/978-3-319-11310-4_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
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