Improving the text classification using clustering and a novel HMM to reduce the dimensionality

A dimensionality reduction method based on document content is proposed.The technique utilizes a document clustering to separate data into groups.It introduces a similarity-based document representation based on a Text HMM.The model is tested with the SVM and k-NN classifiers using two medical corpora.Results show the method outperforms other dimensionality reduction approximations. In text classification problems, the representation of a document has a strong impact on the performance of learning systems. The high dimensionality of the classical structured representations can lead to burdensome computations due to the great size of real-world data. Consequently, there is a need for reducing the quantity of handled information to improve the classification process. In this paper, we propose a method to reduce the dimensionality of a classical text representation based on a clustering technique to group documents, and a previously developed Hidden Markov Model to represent them. We have applied tests with the k-NN and SVM classifiers on the OHSUMED and TREC benchmark text corpora using the proposed dimensionality reduction technique. The experimental results obtained are very satisfactory compared to commonly used techniques like InfoGain and the statistical tests performed demonstrate the suitability of the proposed technique for the preprocessing step in a text classification task.