Earticle

초록

한국어

The identification of key feature selection plays a significant role in accurate population projection, which is an essential aspect of demographic statistics. The goal of this paper is to investigate the importance of the different features in population projection by using four advanced feature analysis techniques i.e. Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA) Fast Independent Component Analysis (FICA) and Principal Component Analysis (PCA). This analysis is important to determine the major factors that affect population change. The identification and ranking of these predictors can enhance demographic forecasting and policy planning. We utilized Koran population data from the UN Population Division dataset and evaluated the above four methods. The experimental results reveal that LDA achieved the lowest performance in selecting the most appropriate features, while PCA is the most efficient in selecting an effective feature with the highest variance. These insights build up the knowledge of population change and refine the projection models.

목차

Abstract
I. INTRODUCTION
II. THE PROPOSED MODEL
A. Canonical Correlation Analysis
B. Principal Component Analysis.
C. Linear Discriminant Analysis.
D. Fast Independent Component Analysis.
III. RESULTS AND EXPERIMENTS
A. Implementation Detail
B. Dataset Explanation
C. Experimental results of the selected methods using targeted dataset.
IV. Conclusion
ACKNOWLEDGMENT
REFERENCES

저자

• Hikmat Yar [ Sejong University ]
• Adnan Hussain [ Sejong University ]
• Noman Khan [ Sejong University ]
• Yun Jung Hur [ Sejong University ]
• Min Je Kim [ Sejong University ]
• Seogbong Jeon [ Sejong University ]
• Sung Wook Baik [ Sejong University ] Corresponding Author

참고문헌