Selective ensemble approach for classification of datasets with incomplete values

Abstract

In some research situations, we often have to classify data with incomplete values which affect the learning performance of classifiers. Although various classification algorithms have been proposed, most of them are short of the ability to deal with incomplete data. This paper proposes a novel approach based on selective ensemble for classifying incomplete data. The method finds the local complete patterns for which the feature values are complete and trains multiple component learners for each local complete subset. Then, it combines the outputs of the classifiers. The method needs no assumption about the incomplete mechanism that is necessary for previous methods. The proposed method is evaluated by three datasets from the UCI Machine Learning Repository. The experiments results show that classification accuracy of the proposed method is superior to those of widely used imputations and deletion method. © 2011 Springer-Verlag Berlin Heidelberg.