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 multiimputation techniqu...

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Vydané v:Advances in Electrical and Computer Engineering Ročník 25; číslo 1; s. 71 - 78
Hlavní autori: JAFFEL, I., GUERFEL, M., MESSAOUD, H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Suceava Stefan cel Mare University of Suceava 01.02.2025
Predmet:
Algorithms
Alternative energy
Artificial intelligence
Cluster analysis
Clustering
Comparative analysis
data preprocessing
Decision tree
Decision trees
Electrical engineering
Energy conversion
Impact analysis
Machine learning
Management
Methods
Missing data
multidimensional signal processing
Regression analysis
Renewable resources
Sensors
statistical analysis
Technology application
Variables
Vector quantization
wind energy
Wind power
Tunisia
ISSN:1582-7445, 1844-7600
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Popis
Shrnutí: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 multiimputation 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. Index Terms--data preprocessing, decision trees, multidimensional signal processing, statistical analysis, wind energy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1582-7445
1844-7600
DOI:10.4316/AECE.2025.01008