The Treatment of Missing Values and its Effect on Classifier Accuracy

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A survey on missing data in machine learning (2021, background): “The other weakness of KNN imputation is that it searches through all the data set, hence increasing computational time [64].”
Multivariate Time Series Imputation with Generative Adversarial Networks (2018, background): “Simple imputation algorithms impute the missing values with some statistical attributes, such as replace missing value with mean value [27], impute with median value [1], impute with most common value [12] and complete the dataset with last observed valid value [2].”
Impact of imputation of missing values on classification error for discrete data (2008, background): “Acuna and Rodriquez [14] have investigated the effect of four methods that deal with missing values.”
Personal or Product Reputation? Optimizing Revenues in the Sharing Economy (2019, background): “Third, although mean imputation provides generally robust estimates especially for datasets having between 15% and 20% of missing values (Acuna and Rodriguez 2004), replacing the missing average review scores with a single value will deflate the variance and partially impact on the distribution of…”