Journal
NEUROCOMPUTING
Volume 72, Issue 7-9, Pages 1483-1493Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2008.11.026
Keywords
Missing data; Pattern classification; Imputation; K nearest neighbours; Mutual information
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Missing data is a common drawback in many real-life pattern classification scenarios. One of the most popular solutions is missing data imputation by the K nearest neighbours (KNN) algorithm. In this article, we propose a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI). This method provides a missing data estimation aimed at solving the classification task, i.e., it provides an imputed dataset which is directed toward improving the classification performance. The MI-based distance metric is also used to implement an effective KNN classifier. Experimental results on both artificial and real classification datasets are provided to illustrate the efficiency and the robustness of the proposed algorithm. (C) 2009 Elsevier B.V. All rights reserved.
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