Using Entropy to Impute Missing Data in a Classification Task

In real applications, part of the data is usually missing. But most techniques of data analysis and data mining can only deal with complete data. In this paper, a new taxonomy of imputation methods is proposed. Within this taxonomy a new technique, based on entropy measures is introduced. Its behaviour is studied through an empirical comparative analysis.