Effect of Missing Data Types and Imputation Methods on Supervised Classifiers: An Evaluation Study

Effect of Missing Data Types and Imputation Methods on Supervised Classifiers: An Evaluation Study

Data completeness is one of the most common challenges that hinder the performance of data analytics platforms. Different studies have assessed the effect of missing values on different classification models based on a single evaluation metric, namely, accuracy. However, accuracy on its own is a mis...

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Bibliographic Details
Main Authors: Menna Ibrahim Gabr, Yehia Mostafa Helmy, Doaa Saad Elzanfaly
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Big Data and Cognitive Computing
Subjects:
data quality
data completeness
missing patterns
imputation techniques
supervised
classifiers
Online Access:https://www.mdpi.com/2504-2289/7/1/55

Internet

https://www.mdpi.com/2504-2289/7/1/55

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