원문정보
보안공학연구지원센터(IJSEIA)
International Journal of Software Engineering and Its Applications
Vol.8 No.9
2014.09
pp.69-80
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보