A multiple criteria evaluation technique for missing values imputation

Missing values are a common problem in most data quality research. Most existing imputation methods of missing values are often evaluated using some distance measure computed between the reference data and the imputed one. Another alternative to evaluate the quality of the imputation is to assess the classification accuracy. In this paper, we address the problem of the evaluation of missing values imputation methods showing that the “best” imputation method according to one criterion is not necessary the “best” according to other criteria. Unfortunately, evaluating an imputation method according to a single aspect is not sufficient. Nevertheless, it is possible that we are interested in the “best” method in terms of two or three aspects simultaneously. This paper proposes different criteria aggregation based on the idea of preserving the characteristics of the original data and considers the evaluation technique of the missing values imputation methods as a Multiple Criteria Decision Making (MCDM) problem. We use the TOPSIS method to implement this multiple criteria evaluation technique. Carried out experiments on benchmark datasets confirm the soundness of our approach.