Is Group Means Imputation Any Better Than Mean Imputation: A Study Using C5.0 Classifier

Abstract

Since most data-driven systems including classifiers require large amounts of complete data, the task of handling missing data has garnered much attention. If one of the variables under study in a dataset has some incomplete values, it is treated as a missing data problem. Various methods in the literature exist for dealing with missing data including complete case analysis, listwise deletion, single imputation and multiple imputations. Out of these, mean imputation remains a favourite for researchers due to its simplicity and ease of use, despite some glaring flaws. In this paper, we compare Mean imputation with a similar single imputation method - Group Means imputation - and present our results on nine real-world datasets with respect to classifier accuracy of the C5.0 classifier on the imputed dataset. We show that while Group Means imputation fares better on training data, the test set accuracies fall in favour of Mean Imputation, which deals with novel data in a much better fashion.