NOVEL ENSEMBLE TECHNIQUES FOR REGRESSION WITH MISSING DATA

In this paper, we consider the problem of missing data, and develop an ensemble-network model for handling the missing data. The proposed method is based on utilizing the inherent uncertainty of the missing records in generating diverse training sets for the ensemble's networks. Specifically we generate the missing values using their probability distribution function. We repeat this procedure many times thereby creating a number of complete data sets. A network is trained for each of these data sets, thereby obtaining an ensemble of networks. Several variants are proposed, and we show analytically that one of these variants is superior to the conventional mean-substitution approach for the limit of large training set. Simulation results confirm the general superiority of the proposed methods compared to the conventional approaches.