An Improved Multi-Imputation Technique Based on Chained Equations and Decision Trees: Application to Wind Energy Conversion Systems

An Improved Multi-Imputation Technique Based on Chained Equations and Decision Trees: Application to Wind Energy Conversion Systems

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Missing data (MD) is a prevalent issue that researchers and data Beyond the Score: Timbre Analysis in Avant-garde Music 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 SOCIAL CONDITIONS AND MATERNAL CONDUCTS IN THE PREVENTION AND MANAGEMENT OF INFANTILE DIARRHEA 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.