Improving the text classification using clustering and a novel HMM to reduce the dimensionality.
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.
Department of Computer Science, Higher Technical School of Computer Engineering, University of Vigo, 32004 Ourense, Spain. Electronic address: adrseara@uvigo.es.
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Seara Vieira
Department of Computer Science, Higher Technical School of Computer Engineering, University of Vigo, 32004 Ourense, Spain. Electronic address: lborrajo@uvigo.es.
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Borrajo
Department of Computer Science, Higher Technical School of Computer Engineering, University of Vigo, 32004 Ourense, Spain. Electronic address: eva@uvigo.es.
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Iglesias
D016000:Cluster Analysis; D008390:Markov Chains; D008962:Models, Theoretical
Dimensionality reduction; Document clustering; Hidden Markov Model; Similarity-based classification; Text classification