MATEC Web Conf.
Volume 204, 2018International Mechanical and Industrial Engineering Conference 2018 (IMIEC 2018)
|Number of page(s)||5|
|Published online||21 September 2018|
The effect of Kurtosis on the accuracy of artificial neural network predictive model
Industrial Engineering Department, Universitas Negeri Malang, 65145 Malang, Indonesia
Corresponding author: firstname.lastname@example.org
This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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