Missing Value Filling Research Based on Ensemble Learning

Jianqiao Sun

Article ID: 5058
Vol 7, Issue 3, 2024

VIEWS - 48 (Abstract) 47 (PDF)

Abstract


This paper studies missing value filling and compares the filling effects of five methods: Mean, KNN, Random Forest, GBDT, and
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


Missing Value Filling; Ensemble Learning; KNN

Full Text:

PDF


References


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

Refbacks

  • There are currently no refbacks.




Creative Commons License

This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.