Open AccessThe relationship between data skewness and accuracy of Artificial Neural Network predictive model
Author(s) -
Aisyah Larasati,
Apif M. Hajji,
Anik Dwiastuti
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/523/1/012070
Subject(s) - skewness , artificial neural network , perceptron , computer science , variable (mathematics) , multilayer perceptron , artificial intelligence , machine learning , statistics , mathematics , mathematical analysis
The purpose of this study is to investigate the relationship between data skewness in the output variable and the accuracy of artificial neural network predictive model. The artificial neural network predictive model is built using multilayer perceptron and consist of one output variable and six input variable, and the algorithm used is back propagation. Data used in this study is generated by conducting the simulations in 1000 cycles. Three categories of skewness used in the output variables are positive skewness, neutral, and negative skewness. The results show that data skewness does not have a significant effect on the accuracy of the artificial neural network predictive model. These results imply that artificial neural network predictive model has a higher capability to cope with skewed data due to its complexity in the hidden layer.