K nearest neighbours with mutual information for simultaneous classification and missing data imputation

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 KK nearest neighbours (KNN)(KNN) algorithm. In this article, we propose a novel KNNKNN 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 KNNKNN classifier. Experimental results on both artificial and real classification datasets are provided to illustrate the efficiency and the robustness of the proposed algorithm.