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A Hybrid Modified Deep Learning Data Imputation Method for Numeric Datasets
2021
International Journal of Intelligent Systems and Applications in Engineering
Missing data is a major problem in terms of both machine learning and data mining methods. Like most of these methods do not work with missing data, negative results may occur on the performance of the working ones, also. Imputation is a data preprocessing method used to replace missing data with appropriate values. This study aims at developing a hybrid modified imputation method based on deep learning approach. For this purpose, we use Random Forest and Datawig deep learning imputation
doi:10.18201/ijisae.2021167931
fatcat:qbgfg3st4vci5nzqz3e5v3m2qy