Missing Value Filling Research Based on Ensemble Learning
Article ID: 5058
Vol 7, Issue 3, 2024
Vol 7, Issue 3, 2024
VIEWS - 2038 (Abstract)
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.
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
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DOI: https://doi.org/10.18686/ijmss.v7i3.5058
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