Mixed-Type Imputation for Missing Data Credal Classification via Quality Matrices

Classification of missing data based on estimation is still challenging since existing methods relying on one imputation strategy fail to consider the diversity of different attribute distributions. In this case, there are inevitably some 'bad' estimations at the attribute level, reducing the performance of classification. This article proposes a mixed-type imputation method (MTI) to classify missing data under the theory of belief functions (TBF) via two quality matrices to address this problem. The proposed MTI method has the advantages of making estimations as close to the truth as possible at the attribute level while reducing the negative impact of possible bad estimations on the classification. Specifically, the first matrix used to impute missing values can characterize the different supports of multiple imputation methods for estimating various attributes. The other matrix used to perform the classification task can extract the reliabilities of estimations on the different classes. The validity has been demonstrated in the final decision support based on the TBF, famous for characterizing uncertainty and imprecision, for example, caused by missing values.