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A Random Patch Algorithm Based on Markov Chain

The Naive Bayesian classifier is the least sensitive to missing data in the methods of data mining. However, missing data still affects the accuracy of the classifiers before the dataset which contains missing data used to train in the classifier. We can fill up the missing data to gain a full dataset. This paper introduces a random patch algorithm based on Markov Transition probability. This algorithm can fill up the dataset which contains missing data to a full one. Using the full dataset to classification can increase the accuracy of the classifier. We perform experiments to prove the algorithm effectiveness.