期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2014
卷号：8
期号：9
页码：69-80
DOI：10.14257/ijseia.2014.8.9.06
出版社：SERSC
摘要：This study investigates ways to improve the performance of rice productivity prediction model by employing the infrequent data binning method. Binning in this study is a technique to reassign infrequent data outside a specific scope, back in to the boundary value of the scope. The main findings of this study include: first, the binning method based on reassigning infrequent data contributes to improving the prediction performance of the model in question. Second, the effects of improvement differ depending on the length of the tail of a distribution. Third, there are no interaction effects due to combination of binned variables involving in different distribution categories with different length of long-tail
关键词：Improvement of Prediction Performance; Infrequent Data Binning; Predicting ; Rice Productivity; Neural Network Method
