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.
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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
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DOI: https://doi.org/10.1007/11875581_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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