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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| Vydáno v: | Advances in Electrical and Computer Engineering Ročník 25; číslo 1; s. 71 - 78 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Suceava
Stefan cel Mare University of Suceava
01.02.2025
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| ISSN: | 1582-7445, 1844-7600 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Audience | Academic |
| Author | JAFFEL, I. MESSAOUD, H. GUERFEL, M. |
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| SubjectTerms | 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 |
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