networks, incomplete data is encountered due to sensor faults, local interference or power outage [@le2005estimating]. In medical fields, patient health care records are often a by-product of patient care activities rather than an organized research protocol which leads to significant loss of information [@cios2002uniqueness]. This leads to almost every patient record lacking some values as well as each attribute/feature having missing values. More than 40% of the datasets in the UCI Machine Learning Repository have missing values [@newman2008uci].

Classification problems are aimed at developing a classifier from training data, so that a new test observation can be correctly classified into one of the groups/classes. The class membership is assumed to be known for each observation of the training set whereas the corresponding attributes/features may have some missing values. The test dataset consists of new observations having the corresponding features but no class labels. The goal of the classification problem is to assign class labels to the test set [@alpaydin2009introduction]. In our problem setup, we assume that some of the features are missing at random (MAR) for the training as well as the test dataset. One approach to classification is ignoring the observations with missing values and building a classifier. This 