Increasing the accuracy of Hidden Naive Bayes model

In this study, we attempt to increase the prediction accuracy of the Hidden Naive Bayes model. Because the concept of combining classifiers is proposed as a new direction for the improvement of the performance of individual classifiers, we make use of Adaboost, with the difference that in each iteration of Adaboost, we replace the missing values, we use a discretization method and we remove redundant features using a filter feature selection method. Finally, we perform a large-scale comparison with other attempts that have tried to improve the accuracy of the simple Bayes algorithm as well as other state-of-the-art algorithms and ensembles on 24 standard benchmark datasets and the present method gives better accuracy in most cases using less time for training, too.