Computer Science Paper Classification for CSAR

  • Jiahui QuanEmail author
  • Qing Li
  • Minglu Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8699)


When researchers or students entering a new research field in computer science, they desire to know who the top scientists are and what the best papers are in this field, then they know to find whom to collaborate with or can find best papers in this area to read. In order to divide different research fields, it is very important to correctly classify all the papers in computer science. In this paper, we propose CSAR classification system derived from 2012 ACM Computing Classification System (CCS), and also propose a new weighted naive Bayes classifier to classify the papers in top publications by their research fields. The experiments show that the performance of proposed weighted naive Bayes classifier is better than the unweighted naive Bayes classifier and overwhelms the results of \(k\)-NN classifier.


Text classification Weighted naive Bayes classifier CSAR classification system Academic information platform 


  1. 1.
    Shi, C., Quan, J., Li, M.: Information extraction for computer science academic rankings system. In: 2013 International Conference on Cloud and Service Computing (2013)Google Scholar
  2. 2.
    Zhao, J., Forouraghi, B.: An interactive and personalized cloud-based virtual learning system to teach computer science. In: 12th International Conference on Web-Based Learning (2013)Google Scholar
  3. 3.
    Kravcik, M., Wan, J.: Towards open corpus adaptive e-learning systems on the web. In: 12th International Conference on Web-Based Learning (2013)Google Scholar
  4. 4.
    2012 ACM Computing Classification System.
  5. 5.
    ACM Digital Library.
  6. 6.
    IEEE Xplore Digital Library.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
    The DBLP Computer Science Bibliography.
  11. 11.
    Coulter, N., French, J., Glinert, E., Horton, T., Mead, N., Rada, R., Ralston, A., Rodkin, C., Rous, B., Tucker, A., Wegner, P., Weiss, E., Wierzbicki, C.: Computing classification system 1998: current status and future maintenance. Comput. Rev. 39(1), 24–39 (1998)Google Scholar
  12. 12.
    Vesseya, I., Ramesha, V., Glassb, R.L.: A unified classification system for research in the computing disciplines. Inf. Softw. Technol. 47(4), 245–255 (2005)CrossRefGoogle Scholar
  13. 13.
    Kashireddy, S.D., Gauch, S., Billah, S.M.: Automatic class labeling for CiteSeerX. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 1, pp. 241–245 (2013)Google Scholar
  14. 14.
    Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short Text classification in Twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2010)Google Scholar
  15. 15.
    Zhang, Z., Lin, H., Li, P., Wang, H., Lu, D.: Improving semi-supervised text classification by using Wikipedia knowledge. In: The 14th International Conference on Web-Age Information Management (2013)Google Scholar
  16. 16.
    Chen, M., Jin, X., Shen, D.: Short text classification improved by learning multi-granularity topics. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 1776–1781 (2011)Google Scholar
  17. 17.
    Thirunavukkarasu, K.S., Sugumaran, S.: Analysis of classification techniques in data mining. Int. J. Eng. Sci. Res. Technol. 3740–3746 (2013)Google Scholar
  18. 18.
    Ibáñez, A., Bielza, C., Larrañaga, P.: Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. Neurocomputing 135, 42–52 (2014)CrossRefGoogle Scholar
  19. 19.
    Neetu: Hierarchical classification of web content using naive Bayes approach. Int. J. Comput. Sci. Eng. 5(5), 402–408 (2013)Google Scholar
  20. 20.
    Wu, J., Cai, Z., Zeng, S., Zhu, X.: Artificial immune system for attribute weighted naive Bayes classification. In: The 2013 International Joint Conference on Neural Networks, pp. 1–8 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.City University of Hong KongHong KongPeople’s Republic of China

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