Abstract
Traditional search engines are not efficient enough to extract useful information from scientific text databases. Therefore, it is necessary to develop advanced information retrieval software tools that allow for further classification of the scientific texts. The aim of this work is to present BioClass, a freely available graphic tool for biomedical text classification. With BioClass an user can parameterize, train and test different text classifiers to determine which technique performs better according to the document corpus. The framework includes data balancing and attribute reduction techniques to prepare the input data and improve the classification efficiency. Classification methods analyze documents by content and differentiate those that are best suited to the user requeriments. BioClass also offers graphical interfaces to get conclusions simply and easily.
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References
Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman (1999)
Tan, S.: Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Systems with Applications 28(4), 667–671 (2005)
Anand, A., Pugalenthi, G., Fogel, G.B., Suganthan, P.N.: An approach for classification of highly imbalanced data using weighting and undersampling. Amino Acids 39, 1385–1391 (2010)
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6(1), 1–6 (2004)
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)
Zhang, J., Mani, I.: knn approach to unbalanced data distributions: A case study involving information extraction. In: Proceedings of the ICML 2003 Workshop on Learning from Imbalanced Datasets (2003)
Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand (April 1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)
Liu, H., Setiono, R.: A probabilistic approach to feature selection - a filter solution. In: 13th International Conference on Machine Learning, pp. 319–327 (1996)
Lorenzo, J.: Selección de Atributos en Aprendizaje Automático basado en la Teoría de la Información. PhD thesis, Faculty of Computer Science, Univ. of Las Palmas (2002)
Holte, R.C.: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning 11(1), 63–90 (1993)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer (2002)
Hirschman, L., Yeh, A., Blaschke, C., Valencia, A.: Overview of biocreative: critical assessment of information extraction for biology. BMC Bioinformatics 6(suppl.1), s1 (2005)
Ando, R.K., Dredze, M., Zhang, T.: Trec 2005 genomics track experiments at ibm watson. In: Proceedings of TREC 2005. NIST Special Publication (2005)
Collier, N., Hahn, U., Rebholz-Schuhmann, D., Rinaldi, F., Pyysalo, S. (eds.): Proceedings of the Fourth International Symposium for Semantic Mining in Biomedicine, Cambridge, United Kingdom. CEUR Workshop Proceedings, vol. 714. CEUR-WS.org (October 2010)
Osuna, E., Freund, R., Girosi, F.: Support vector machines: Training and applications. Technical report, Cambridge, MA, USA (1997)
Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning 29, 103–130 (1997)
Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1991)
Glez-Peña, D., Reboiro-Jato, M., Maia, P., Rocha, M., Díaz, F., Fdez-Riverola, F.: Aibench: A rapid application development framework for translational research in biomedicine. Comput. Methods Prog. Biomed. 98, 191–203 (2010)
Chang, C., Lin, C.: LIBSVM: a library for support vector machines (2001)
Demšar, J., Zupan, B., Leban, G., Curk, T.: Orange: From experimental machine learning to interactive data mining. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 537–539. Springer, Heidelberg (2004)
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Romero, R., Vieira, A.S., Iglesias, E.L., Borrajo, L. (2014). BioClass: A Tool for Biomedical Text Classification. In: Saez-Rodriguez, J., Rocha, M., Fdez-Riverola, F., De Paz Santana, J. (eds) 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014). Advances in Intelligent Systems and Computing, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-319-07581-5_29
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DOI: https://doi.org/10.1007/978-3-319-07581-5_29
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