@Article{csse.2019.34.145,
AUTHOR = {Abdullah Erdal Tümer, Aytekin Akku¸s},
TITLE = {Application of Radial Basis Function Networks with Feature Selection for GDP Per Capita Estimation Based on Academic Parameters},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {34},
YEAR = {2019},
NUMBER = {3},
PAGES = {145--150},
URL = {http://www.techscience.com/csse/v34n3/40035},
ISSN = {},
ABSTRACT = {In this work, a system based on Radial Basis Function Network was developed to estimate Gross Domestic Product per capita. The data set based on 180
academic parameters of 13 Organisation for Economic Co-operation and Development countries was used to verify the effectiveness and accuracy of the
proposed method. Gross Domestic Product per capita was studied to be estimated for the first time with academic parameters in this study. The system
has been optimized using feature selection method to eliminate unimportant features. Radial Basis Function network results and Radial Basis Function
network with feature selection method results were compared. The findings show that the Radial Basis Function network with feature selection is 10% more
successful than the Radial Basis Function results. Based on results, this methodology can be applied in applications of Gross Domestic Product per capita
forecasting.},
DOI = {10.32604/csse.2019.34.145}
}