A new dimensionality reduction technique based on HMM for boosting document classification

Abstract

Many classification problems, such as text classification, require the ability to handle the high dimension of a structured representation of the documents. The enormous size of the data would result in burdensome computations. Consequently, there is a strong need for reducing the quantity of handled information to develop the classification process. In this paper, we propose a dimensionality reduction technique on text datasets based on a clustering method to group documents with a simple Hidden Markov Model to represent them. We have applied the new method on the OHSUMED benchmark text corpora using the k-NN and SVM classifiers. The results obtained are very satisfactory and demonstrate the suitability of the proposed technique for the problem of dimensionality reduction and document classification.