Abstract
Multi-view algorithms such as co-training improve the accuracy of text classification because they optimize the functions to exploit different views of the same input data. However, despite being more promising than the single-view approaches, document datasets often have no natural multiple views available.
This study proposes an HMM-based algorithm to generate a new view from a standard text dataset, and a co-training framework where this view generation is applied. Given a dataset and a user classifier model as input, the goal of our framework is to improve the classifier performance by increasing the labelled document pool, taking advantage of the multi-view semi-supervised co-training algorithm.
The novel architecture was tested using two different standard text corpora: Reuters and 20 Newsgroups and a classical SVM classifier. The results obtained are promising, showing a significant increase in the efficiency of the classifier compared to a single-view approach.
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Acknowledgements
This work has been funded from the European Union Seventh Framework Programme [FP7/REGPOT-2012-2013.1] under grant agreement n 316265, BIOCAPS, the “Platform of integration of intelligent techniques for analysis of biomedical information” project (TIN2013-47153-C3-3-R) from Spanish Ministry of Economy and Competitiveness and the [14VI05] Contract-Programme from the University of Vigo.
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Iglesias, E.L., Vieira, A.S., Diz, L.B. (2016). An HMM-Based Multi-view Co-training Framework for Single-View Text Corpora. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_6
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