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A Bayesian approach to the missing features problem in classification

In this paper, the Bayesian data reduction algorithm (BDRA) is extended to classify discrete test observations given the training data contains feature vectors which are missing values. Two methods are used to model missing features in the BDRA, where performance is compared to a neural network using both simulated and real data. In general, it is shown that the BDRA is superior to the neural network.