is only feasible when the missingness is insignificant, however, and it has been demonstrated that even with a 5% missingness, proper imputation increases the classification accuracy [@farhangfar2008impact]. We focus on imputation of missing values in the training as well as the test dataset so as to improve the overall performance of the classifier on the test data. Our proposed method takes into account the class label during imputation of the training features, and this ensures an overall improvement in classification.

The work related to missing data imputation can be divided into two categories, single imputation and multiple imputation. Single imputation strategies provide a single value for the missing datum. The earliest single value imputation strategy was Mean Imputation [@little2002] which ignores the input data distribution by imputing just one value for all missing instances of a feature. Other popular single imputation techniques are hot deck and cold deck imputation [@little2002], C4.5 [@quinlan1993c4] and prediction based models [@schafer1997analysis]. C4.5 works well with discrete data but not with numerical data, which has to be discretized to apply the algorithm [@tsai2008discretization]. Prediction based models depend on the correct modelling of missing features and the relationship between them. Usually, incomplete datasets obtained from studies 