Advertisement

Multiple Support Vector Machines for Binary Text Classification Based on Sliding Window Technique

  • Aisha Rashed AlbqmiEmail author
  • Yuefeng Li
  • Yue Xu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)

Abstract

Supervised machine learning algorithms, such as support vector machines (SVMs), are widely used for solving classification tasks. In binary text classification, linear SVM has shown remarkable efficiency for classifying documents due to its superior performance. It tries to create the best decision boundary that enables the separation of positive and negative documents with the largest margin hyperplane. However, in most cases there are regions in which positive and negative documents are mixed due to the uncertain boundary. With an uncertain boundary, the learning classifier is more complex, and it often becomes difficult for a single classifier to accurately classify all unknown testing samples into classes. Therefore, more innovative methods and techniques are needed to solve the uncertain boundary problem that was traditionally solved by non-linear SVM. In this paper, multiple support vector machines are proposed that can effectively deal with the uncertain boundary and improve predictive accuracy in linear SVM for data having uncertainties. This is achieved by dividing the training documents into three distinct regions (positive, boundary, and negative regions) based on a sliding window technique to ensure the certainty of extracted knowledge to describe relevant information. The model then derives new training samples to build a multiple SVMs based classifier. The experimental results on the TREC topics and standard dataset Reuters Corpus Volume 1 (RCV1), indicated that the proposed model significantly outperforms six state-of-the-art baseline models in binary text classification.

Keywords

Support Vector Machines Binary text classification Uncertain boundary Sliding window technique 

References

  1. 1.
    Lata, S., Loar, M.R.: Text clustering and classification techniques using data mining. Int. J. on Futur. Revolut. Comput. Sci. Commun. Eng. 4(4), 859–864 (2018)Google Scholar
  2. 2.
    Joachims, T.: Transductive inference for text classification using support vector machines. In: ICML 1999, pp. 200–209. ACM, San Francisco (1999)Google Scholar
  3. 3.
    John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: UAI 1995, pp. 338–345. ACM, Canada (1995)Google Scholar
  4. 4.
    Aggarwal, C.C., Zhai, C.: A Survey of Text Classification Algorithms. In: Mining Text Data, pp. 163–222. Springer, Boston, (2012).  https://doi.org/10.1007/978-1-4614-3223-4_6CrossRefGoogle Scholar
  5. 5.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  6. 6.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)zbMATHGoogle Scholar
  8. 8.
    Zhang, L., Li, Y., Bijaksana, M.A.: Decreasing uncertainty for improvement of relevancy prediction. In: Proceeding of the Twelfth Australasian Data Mining Conference, AusDM 2014, Brisbane, pp. 157–162 (2014)Google Scholar
  9. 9.
    Li, Y., Zhang, L., Yue, X., Yiyu, Y., Raymond, L., Yutong, W.: Enhancing binary classification by modeling uncertain boundary in three-way decisions. IEEE Trans. Knowl. Data Eng. 29(7), 1438–1451 (2017)CrossRefGoogle Scholar
  10. 10.
    Wardaya, P.D.: Support vector machine as a binary classifier for automated object detection in remotely sensed data. In: IOP Conference Series: Earth and Environmental Science, vol. 18, no. 1. IOP Publishing, Bristol (2014)Google Scholar
  11. 11.
    Wei, L., Wei, B., Wang, B.: Text classification using support vector machine with mixture of Kernel. J. Softw. Eng. Appl. 5(12), 55–58 (2012)CrossRefGoogle Scholar
  12. 12.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0026683CrossRefGoogle Scholar
  13. 13.
    Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)CrossRefGoogle Scholar
  14. 14.
    Shannon, M.: Forensic relative strength scoring: ASCII and entropy scoring. Int. J. Digit. Evid. 2(4), 1–19 (2004)Google Scholar
  15. 15.
    Lau, R.Y., Bruza, P.D., Song, D.: Towards a belief-revision-based adaptive and context-sensitive information retrieval system. ACM Trans. Inf. Syst. (TOIS). 26(2), 1–38 (2008)CrossRefGoogle Scholar
  16. 16.
    Bekkerman, R., Gavish, M.: High-precision phrase-based document classification on a modern scale. In: KDD 2011, pp. 231–239. ACM, San Diego (2011)Google Scholar
  17. 17.
    Li, Y., Algarni, A., Zhong, N.: Mining positive and negative patterns for relevance feature discovery. In: KDD 2010, pp. 753–762. ACM, New York (2010)Google Scholar
  18. 18.
    Fu, Z., Robles-Kelly, A., Zhou, J.: Mixing linear SVMs for nonlinear classification. IEEE Trans. Neural Netw. 21(12), 1963–1975 (2010)CrossRefGoogle Scholar
  19. 19.
    Rodriguez-Lujan, I., Cruz, C.S., Huerta, R.: Hierarchical linear support vector machine. Pattern Recogn. 45(12), 4414–4427 (2012)CrossRefGoogle Scholar
  20. 20.
    Gao, Y., Sun, S.: An empirical evaluation of linear and nonlinear kernels for text classification using support vector machines. In: FSKD 2010, pp. 1502–1505. IEEE, Yantai (2010)Google Scholar
  21. 21.
    Lan, M., Tan, C.L., Low, H.B.: Proposing a new term weighting scheme for text categorization. In: AAAI 2006, pp. 763–768. ACM, Boston (2006)Google Scholar
  22. 22.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical Report, Department of Computer Science, National Taiwan University, Taipei (2003)Google Scholar
  23. 23.
    Du, L., Song, Q., Jia, X.: Detecting concept drift: an information entropy based method using an adaptive sliding window. Intell. Data Anal. 18(3), 337–364 (2014)CrossRefGoogle Scholar
  24. 24.
    Robertson, S., Zaragoza, H.: The Probabilistic Relevance Framework: BM25 and Beyond. Now Publishers Inc., Breda (2009)Google Scholar
  25. 25.
    Ko, Y.J., Seo, J.Y.: Issues and empirical results for improving text classification. J. Comput. Sci. Eng. 5(2), 150–160 (2011)CrossRefGoogle Scholar
  26. 26.
    Hall, G.A.: Sliding window measurement for file type identification. Technical Report, ManTech Security and Mission Assurance (2006)Google Scholar
  27. 27.
    Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)Google Scholar
  28. 28.
    Joachims, T.: A support vector method for multivariate performance measures. In: ICML 2005, pp. 377–384. ACM, Germany (2005)Google Scholar
  29. 29.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of EECSQueensland University of TechnologyBrisbaneAustralia
  2. 2.Department of CSTaif UniversityTaifSaudi Arabia

Personalised recommendations