Nonparametric Imputation Framework for Missing Values in Datasets
Missing values may occur for several reasons, such as malfunctioning of measurement equipment, changes in experimental design during data collection, collation of several similar but not identical datasets and also when respondents in a survey may refuse to answer certain questions such as age or income. Missing values in datasets can be taken as a common problem in statistical analysis. This paper first proposes the analysis of broadly used methods to treat missing values which are either continuous or discrete. And then, an estimator is advocated to impute both continuous and discrete missing target values. The proposed method is evaluated to demonstrate that the approach is better than existing methods in terms of classification accuracy.