An Improved Multi-Imputation Technique Based on Chained Equations and Decision Trees: Application to Wind Energy Conversion Systems
Missing data (MD) is a prevalent issue that researchers and data scientists frequently encounter. It can significantly impact the quality of analyzed data, affecting the relevance of the interpreted results and the inferred conclusions. In response to this challenge, a novel multi-imputation techn...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2025-02-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2025.01008 |
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| Summary: | Missing data (MD) is a prevalent issue that researchers and data scientists frequently encounter. It can significantly
impact the quality of analyzed data, affecting the relevance of the interpreted results and the inferred conclusions.
In response to this challenge, a novel multi-imputation technique that combines Multivariate Imputation by Chained
Equation (MICE) with Decision Tree (DT), namely (MICE-DT), is proposed. This developed method was evaluated against
several established imputation techniques, including K-Nearest Neighbors (KNN), K-Means clustering, Decision Tree
(DT), and MICE, under the assumption of Missing at Random (MAR). The performance of the MICE-DT algorithm, along
with the comparative analysis of the studied techniques, was demonstrated on a Wind Energy Conversion System (WEC),
yielding satisfactory results. |
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| ISSN: | 1582-7445 1844-7600 |