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Missing values in tabular data restrict the use and performance of machinelearning, requiring the imputation of missing values. The most popularimputation algorithm is arguably multiple imputations using chains of equations(MICE), which estimates missing values from linear conditioning on observedvalues. This paper proposes methods to improve both the imputation accuracy ofMICE and the classification accuracy of imputed data by replacing MICE's linearconditioning with ensemble learning and deep neural networks (DNN). Theimputation accuracy is further improved by characterizing individual sampleswith cluster labels (CISCL) obtained from the training data. Our extensiveanalyses involving six tabular data sets, up to 80% missingness, and threemissingness types (missing completely at random, missing at random, missing notat random) reveal that ensemble or deep learning within MICE is superior to thebaseline MICE (b-MICE), both of which are consistently outperformed by CISCL.Results show that CISCL plus b-MICE outperforms b-MICE for all percentages andtypes of missingness. Our proposed DNN based MICE and gradient boosting MICEplus CISCL (GB-MICE-CISCL) outperform seven other baseline imputationalgorithms in most experimental cases. The classification accuracy on the dataimputed by GB-MICE is improved by proposed GB-MICE-CISCL imputed data acrossall missingness percentages. Results also reveal a shortcoming of the MICEframework at high missingness (>50%) and when the missing type is not random.This paper provides a generalized approach to identifying the best imputationmodel for a data set with a missingness percentage and type.

Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework