Efficient Classification Method for Complex Biological Literature Using Text and Data Mining Combination

  • Yun Jeong Choi
  • Seung Soo Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


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.


Text Mining Text Classification Decision Boundary Target Category Data Mining Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yun Jeong Choi
    • 1
  • Seung Soo Park
    • 1
  1. 1.Department of Computer Science & EngineeringEwha Womans UniversitySeoulKorea

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