Advertisement

An Automatic Library Data Classification System Using Layer Structure and Voting Strategy

  • June-Jei Kuo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8839)

Abstract

This paper deals with issues of traditional one-layered book classification systems and employs the complementary attribute of various classifiers to propose a two layered book classification system using voting strategy. Moreover, the collection of dissertations from a university library and books from an electronic bookstore are used as the training and testing corpus. The classification codes of dissertations and books are employed as the gold standard as well. Each dissertation contains various components such as title, authors, table of contents, abstract or cited papers et al. To understand the classification effect of all the combinations of components, various combinations are studied as well and the best combination is recommended. The features extracted from abstracts and table of content are found to be most useful for document classification. On the other hand, to obtain the best classification performance, the combination of classifiers for a two-layered book classification system is studied and the best combination was also recommended as well.

Keywords

Support Vector Machine Classification Performance Vote Strategy Automatic Classification Document Classification 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, K.H., Lo, S.C., Lin, C.J.: The Investigation of the Consistency of Subject Cataloging for Academic Journal Articles of Library and Information Science. In: Proceedings of Information and Communication Conference, Taipei, pp. 125–142 (2002) (Chinese)Google Scholar
  2. 2.
    Chen, S.Y., Yeh, J.Y., Hwang, M.J., Lin, X.J., Ke, H.R., Yang, W.P.: Automatic Book Classification Method combined with Support Vector Machine and Metadata. International Journal of Advanced Information Technologies (IJAIT) 3(1), 2–21 (2009) (Chinese)Google Scholar
  3. 3.
    AL-Nabi, D.L.A., Ahmed, S.: Survey on Classification Algorithms for Data Mining(Comparison and Evaluation). Computer Engineering and Intelligent Systems 4(8), 18–24 (2013)Google Scholar
  4. 4.
    Farbrizio, S.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  5. 5.
    Huang, C.M.: A Neural Network Approach to Automatic Classification of Thesis Documents.,Report of National Science Council (NSC 89-2416-H-224-053), Taiwan (2002) (Chinese)Google Scholar
  6. 6.
    Huang, J.H.: A Study of Book Title Feature Extraction Based on the Automatic Classification -An Example of BibliographyAutomatically Classified System.Unpublished Master Thesis, Department of Library and Information Science of Fu-Jen Catholic University, Taipei (2008) (Chinese)Google Scholar
  7. 7.
    Larson, R.R.: Experiments in Automatic Library of Congress Classification. Journal of the American Society for Information Science 43(2), 130–148 (1992)CrossRefGoogle Scholar
  8. 8.
    Magdy, W., Darwish, K.: Book Search: Indexing the Valuable Parts. In: Proceedings of the 2008 Workshop on Research Advances in Large Digital Book Repositories, pp. 53–56 (2008)Google Scholar
  9. 9.
    Tokkola, K.: Discriminative Features for Document Classification. In: Proceedings of the 16th International Conference on Pattern Recognition (ICPR 2002), vol. 1, pp. 472–475 (2002)Google Scholar
  10. 10.
    Wang, et al.: Complementary classification approaches for protein sequences. Protein Engineering 9(5), 381–386 (1996)CrossRefGoogle Scholar
  11. 11.
    Wu, et al.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 1–37 (2008)CrossRefGoogle Scholar
  12. 12.
    Yi, K.: Challenges in Automatic Classification using Library Classification Schemes. In: Proceedings of World Library and Information Congress: 72nd IFLA General Conference and Council, pp. 1–14 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • June-Jei Kuo
    • 1
  1. 1.Graduate Institute of Library and Information ScienceNational Chung Hsing UniversityTaichungTaiwan

Personalised recommendations