Increasing the Accuracy of Discriminative of Multinomial Bayesian Classifier in Text Classification

Text classification plays an important role in information extraction and summarization, text retrieval, and question-answering. The discriminative multinomial naive Bayes classifier has been a focus of research in the field of text classification. This paper increases the accuracy of discriminative multinomial Bayesian classifier with the usage of the feature selection technique that evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class. We performed a large-scale comparison on benchmark datasets with other state-of-the-art algorithms and the proposed methodology had greater accuracy in most cases.