Earticle

초록

영어

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

목차

Abstract
 1. Introduction
 2. Prediction Algorithm Development and Neural Network Method
 3. Impact Factors on Rice Productivity
 4. Research Process and Data Collection
 5. Prediction Model Development and Optimization
 6. Infrequent Data Binning
 7. Prediction Performance Comparison
 8. Conclusions and Discussion
 Acknowledgments
 References

키워드

저자

• Ik Hoon Jang [ Dept. of Agricultural Economics and Rural Development, Seoul National University ] First author
• Young Chan Choe [ Dept. of Agricultural Economics and Rural Development, Seoul National University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605