VCI predictors: Voting on classifications from imputed learning sets

Abstract

We propose VCI (voting on classifications from imputed learning sets) predictors, which generate multiple incomplete learning sets from a complete dataset by randomly deleting values with a small MCAR (missing completely at random) missing ratio, and then apply an imputation technique to fill in the missing values before giving the imputed data to a machine learner. The final prediction of a class is the result of voting on the classifications from the imputed learning sets. Our empirical results show that VCI predictors significantly improve the classification performance on complete data, and perform better than Bagging predictors on binary class data.