Missing Value Filling Research Based on Ensemble Learning
Vol 7, Issue 3, 2024
VIEWS - 48 (Abstract) 47 (PDF)
Abstract
Stacking under different missing proportions, proving the superiority of ensemble learning algorithms in filling performance when multiple
feature values are missing. Then the missing value filling method of KNN+integrated learning is proposed to further improve the filling performance.
Keywords
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1. [1] Zheng Zhiquan, Wang Mengmeng, Tian Weiqi. Research on Missing data filling based on weighted K-nearest neighbor algorithm
2. [J]. Intelligent Computers and Applications, 2021, 11(11).
3. [2] Du Yingkui, Zhang Yifang, Yuan Zhonghu, et al. Analysis of air pollution prediction accuracy of LSTM network by data Preprocessing [J]. Computer and Digital Engineering, 2021, 49(7).
4. [3] Zhang Xiaoqin, Cheng Yuying. Missing value filling method of component data based on random forest model [J]. Applied Probability and Statistics,2017,33 (1).
5. [4] Atiq R, Fariha F, Mahmud M, et al. A Comparison of Missing Value Imputation Techniques on Coupon Acceptance Prediction[J].
6. International Journal of Information Technology and Computer Science(IJITCS), 2022, 14(5).
7. [5] Zhang Mingwei, Zhang Tianyi, Zhong Ming, et al. The significance of arterial damage in the early detection of diabetes mellitus
8. verified by the integrated learning algorithm Stacking [J]. Chinese Journal of Medical Physics,2022,39(8).
9. [6] Shi Yuntao, Ren Peng, Li Shuqin, et al. Safety risk analysis and prediction of active meat and meat products based on Ensemble
10. learning [J]. Journal of Food Safety and Quality Inspection, 2019,13(16).
DOI: https://doi.org/10.18686/ijmss.v7i3.5058
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