Data cleaning using predictive techniques - Malak Hassan Mostafa ElBakry; Sherif Mazen; Ayman El-Kilany; Farid Ali (2025)
Handling missing values in time series data is a crucial challenge in data analysis.This thesis addresses the challenge of missing values in time series data by proposing two novel imputation techniques. The first uses fuzzy logic, utilizing fuzzy Gaussian and fuzzy triangular membership functions to determine the imputed values based on weighted nearest neighbor. Results show that the fuzzy triangular yield superior accuracy compared to the others. The second approach introduces a predictive ensemble framework called Predictive Missing Values via Voting Ensemble (PMVVE)that integrates multiple classifiers, outperforming traditional methods in accuracy and reliability across real-world datasets.
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Thesis (Ph.D)-Cairo University, 2025.
Bibliography: pages 78-84.
Issues also as CD.
Text in English and abstract in Arabic & English.
Keywords: Data processing; (3eYGdLI GdHjGfGJ s; Data Quality -- Missing Data -- Machine Learning -- Fuzzy Logic -- Time Series Data -- Data Imputation