Abstract
Recently, as the size of genetic knowledge grows faster, the automated analysis and systemization into high-throughput database has become a hot issue. In bioinformatics area, one of the essential tasks is to recognize and identify genomic entities and discover their relations from various sources. Generally, biological literatures containing ambiguous entities, are laid by decision boundaries. The purpose of this paper is to design and implement a classification system for improving performance in identifying entity problems. The system is based on reinforcement training and post-processing method and supplemented by data mining algorithms to enhance its performance. For experiments, we add some intentional noises to training data for testing the robustness and stability. The result shows significantly improved stability on training errors.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Bayardo, R., Srikant, R.: Athena: Mining-based Interactive Management of Text Databases. In: Proc. of the International Conference on Extending Database Technology, pp. 365–379 (2000)
Koller, D., Tong, S.: Active learning for parameter estimation in Bayesian networks. Neural Information Processing Systems (2001)
Liu, B., Wu, H., Phang, T.H.: a Refinement Approach to Handling Model Misfit in Text Categorization. In: SIGKDD (2002)
Castillo, M.D., Serrano, J.L.: A Multistrategy Approach for Digital Text Categorization form Imbalanced Documents. In: SIGKDD, vol. 6, pp. 70–79 (2004)
Gao, S., Wu, W., et al.: A MFoM Learning Approach to Robust Multiclass Multi-Label Text Categorization. In: Proceedings of the 21th Intenational Conference on Machine Learning (2004)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Hasenager, M.: Active Data Selection in Supervised and Unsupervised Learning. PhD thesis, Technische Fakultat der Universitat Bielefeld (2000)
Hatzivassiloglou, V., Duboue, P.A., Rzhetsky, A.: Disambiguating Proteins, Genes and RNA in Text: a Machine Learning Approach. Bioinformatics 17, S97–S106 (2001)
Chen, L., Liu, H., Friedman, C.: Gene Name Ambiguity of Eukaryotic Nomenclatures. Bioinformatics 21(2), 248–256 (2005)
BOW toolkit, http://www.cs.cmu.edu/~mccallum/bow/
Choi, Y.J., Park, S.S.: Refinement Method of Post-processing and Training for Improvement of Automated Text Classification. In: Proc. of the International Conference, ICCSA (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Choi, Y.J., Park, S.S. (2006). Efficient Classification Method for Complex Biological Literature Using Text and Data Mining Combination. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_83
Download citation
DOI: https://doi.org/10.1007/11875581_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)