EACImpute: An Evolutionary Algorithm for Clustering-Based Imputation

We describe an imputation method (EACImpute) that is based on an evolutionary algorithm for clustering. This method relies on the assumption that clusters of (partially unknown) data can provide useful information for imputation purposes. Experimental results obtained in 5 data sets illustrate different scenarios in which EACImpute performs similarly to widely used imputation methods, thus becoming eligible to join a pool of methods to be used in practical applications. In particular, imputation methods have been traditionally only assessed by some measures of their prediction capability. Although this evaluation is useful, we here also discuss the influence of imputed values in the classification task. Finally, our empirical results suggest that better prediction results do not necessarily imply in less classification bias.