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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A New Dimensionality Reduction Technique Based on HMM for Boosting Document Classification
verfasst von : A. Seara Vieira, E. L. Iglesias, L. Borrajo
Erschienen in: 9th International Conference on Practical Applications of Computational Biology and Bioinformatics
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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.