A Bayesian Classifiers based Combination Model for Automatic Text Classification

Text classification deals with allocating a text document to a predetermined class. Generally, this involves learning about a class from representations of documents belonging to that class. In this paper, we propose a classifier combination that uses a Multinomial Naïve Bayesian (MNB) classifier along with Bayesian Networks (BN) classifier. The results of two classifiers are combined by taking an average of the probability distributions calculated by each of the two classifiers. Feature extraction and selection techniques have been incorporated with the model to find the most discriminating terms for classification. This classification model has been tested on three real text datasets. According to experiments, this approach showed better performance and the overall accuracy is higher than the accuracies of the two constituent classifiers. This technique also surpasses the accuracy of other well known, standard classifiers. This approach differs from the previous classification techniques in that it successfully incorporates MNB and BN classifiers and shows significantly better results than using either of the two classifiers separately. A comparative study of previous approaches with our method indicates a significant improvement over a number of techniques that were evaluated on the same dataset.