Abstract
In this study we analyze several data balancing techniques and attribute reduction algorithms and their impact over the information retrieval process. Specifically, we study its performance when used in biomedical text classification using Support Vector Machines (SVMs) based on Linear, Radial, Polynomial and Sigmoid kernels. From experiments on the TREC Genomics 2005 biomedical text public corpus we conclude that these techniques are necessary to improve the classification process. Kernels get some improvements about their results when attribute reduction algorithms were used.Moreover, if balancing techniques and attribute reduction algorithms are applied, results obtained with oversampling are better than subsampling.
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Barandela, R., Sánchez, J.S., GarcÃa, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36(3), 849–851 (2003)
Borrajo, L., Romero, R., Iglesias, E.L., Redondo Marey, C.M.: Improving imbalanced scientific text classification using sampling strategies and dictionaries. Journal of Integrative Bioinformatics 8(3) (2011)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)
Cunningham, H., Wilks, Y., Gaizauskas, R.J.: Gate - a general architecture for text engineering. In: COLING, pp. 1057–1060 (1996)
Garner, S.R.: Weka: The waikato environment for knowledge analysis. In: Proc. of the New Zealand Computer Science Research Students Conference, pp. 57–64 (1995)
Hersh, W., Cohen, A., Yang, J., Bhupatiraju, R.T., Roberts, P., Hearst, M.: Trec 2005 genomics track overview. In: Voorhees, E.M., Buckland, L.P. (eds.) Proceedings of the Fourteenth Text REtrieval Conference, TREC 2005, Special Publication 500-266, pp. 14–25. National Institute of Standards and Technology, NIST (2005)
Kang, P., Cho, S.: EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4232, pp. 837–846. Springer, Heidelberg (2006)
Romero, R., Iglesias, E.L., Borrajo, L.: Building biomedical text classifiers under sample selection bias. In: Abraham, A., Corchado, J.M., RodrÃguez-González, S., Santana, J.F.D.P. (eds.) DCAI. AISC, vol. 91, pp. 11–18. Springer, Heidelberg (2011)
Romero, R., Iglesias, E.L., Borrajo, L., Marey, C.M.R.: Using dictionaries for biomedical text classification. Advances in Intelligent and Soft Computing 93, 365–372 (2011)
Settles, B.: Abner: An open source tool for automatically tagging genes, proteins, and other entity names in text. Bioinformatics 21(14), 3191–3192 (2005)
Tan, S.: Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Systems with Applications 28(4), 667–671 (2005)
Voorhees, E.M., Buckland, L.P. (eds.): Proceedings of the Fourteenth Text REtrieval Conference,TREC 2005, vol. Special Publication 500-266. National Institute of Standards and Technology, NIST (2005)
Weiss, G.M.: Mining with rarity: a unifying framework. SIGKDD Explor. Newsl. 6, 7–19 (2004)
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Romero, R., Iglesias, E.L., Borrajo, L. (2012). A Comparative Analysis of Balancing Techniques and Attribute Reduction Algorithms. In: Rocha, M., Luscombe, N., Fdez-Riverola, F., RodrÃguez, J. (eds) 6th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent and Soft Computing, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28839-5_10
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DOI: https://doi.org/10.1007/978-3-642-28839-5_10
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