An HMM-based text classifier less sensitive to document management problems

Abstract

Background: The performance of the text classification techniques is commonly affected by the characteristics and representation of the document corpora itself. Of all the problems arising from the corpus, there are three major difficulties which the classifiers must deal with: the feature selection issues, the class imbalance problem and the size of the training set. Objective: The objective of this paper is to present a novel based-content text classifier called T-LHMM that is less sensitive to the text representation and the size of the corpus, and more efficient in terms of running time than other classification techniques. Method: In order to demonstrate it, we present a set of experiments performed on well-known biomedical text corpora. We also compare our classifier with k-Nearest Neighbours and Support Vector Machine models. Results and Conclusion: The experimental and statistical results show that the proposed HMM-based text classifier is indeed less sensitive to the class imbalance, the size of the corpus and the vocabulary than the other classifiers. In addition, it is more efficient in terms of running time than k-NN and SVM techniques.