Efficient Classification Method for Complex Biological Literature Using Text and Data Mining Combination
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.
KeywordsText Mining Text Classification Decision Boundary Target Category Data Mining Algorithm
Unable to display preview. Download preview PDF.
- 1.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)Google Scholar
- 2.Koller, D., Tong, S.: Active learning for parameter estimation in Bayesian networks. Neural Information Processing Systems (2001)Google Scholar
- 3.Liu, B., Wu, H., Phang, T.H.: a Refinement Approach to Handling Model Misfit in Text Categorization. In: SIGKDD (2002)Google Scholar
- 4.Castillo, M.D., Serrano, J.L.: A Multistrategy Approach for Digital Text Categorization form Imbalanced Documents. In: SIGKDD, vol. 6, pp. 70–79 (2004)Google Scholar
- 5.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)Google Scholar
- 7.Hasenager, M.: Active Data Selection in Supervised and Unsupervised Learning. PhD thesis, Technische Fakultat der Universitat Bielefeld (2000)Google Scholar
- 8.Hatzivassiloglou, V., Duboue, P.A., Rzhetsky, A.: Disambiguating Proteins, Genes and RNA in Text: a Machine Learning Approach. Bioinformatics 17, S97–S106 (2001)Google Scholar
- 10.BOW toolkit, http://www.cs.cmu.edu/~mccallum/bow/
- 11.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)Google Scholar