Advertisement

Classification Using Various Machine Learning Methods and Combinations of Key-Phrases and Visual Features

  • Yaakov HaCohen-Kerner
  • Asaf Sabag
  • Dimitris Liparas
  • Anastasia Moumtzidou
  • Stefanos Vrochidis
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

In this paper, we present a comparative study of news documents classification using various supervised machine learning methods and different combinations of key-phrases (word N-grams extracted from text) and visual features (extracted from a representative image from each document). The application domain is news documents written in English that belong to four categories: Health, Lifestyle-Leisure, Nature-Environment and Politics. The use of the N-gram textual feature set alone led to an accuracy result of 81.0 %, which is much better than the corresponding accuracy result (58.4 %) obtained through the use of the visual feature set alone. A competition between three classification methods, a feature selection method, and parameter tuning led to improved accuracy (86.7 %), achieved by the Random Forests method.

Keywords

Document classification Feature selection Key-phrases N-gram features Supervised learning Visual features 

Notes

Acknowledgments

This work was supported by MULTISENSOR project, partially funded by the European Commission, under the contract number FP7-610411. The authors would also like to thank Avi Rosenfeld, Maor Tzidkani and Daniel Nissim Cohen from the Jerusalem College of Technology, Lev Academic Center, for their assistance to the authors in providing the software tool to generate the textual features used in this research. The authors would also like to acknowledge the networking support by the COST Action IC1302: semantic KEYword-based Search on sTructured data sOurcEs (KEYSTONE) and the COST Action IC1307: The European Network on Integrating Vision and Language (iV&L Net).

References

  1. 1.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar
  2. 2.
    Ozgür, A.: Supervised and unsupervised machine learning techniques for text document categorization. Doctoral dissertation, Bogaziçi University (2004)Google Scholar
  3. 3.
    Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Informatica 31, 249–268 (2007)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Pazienza, M.T.: Information Extraction A Multidisciplinary Approach to an Emerging Information Technology. LNCS, vol. 1299. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  6. 6.
    Sebastiani, F.: Text categorization. In: Zanasi, A. (ed.) Text Mining and its Applications to Intelligence. CRM and Knowledge Management, pp. 109–129. WIT Press, Southampton (2005)CrossRefGoogle Scholar
  7. 7.
    Kim, S.M., Hovy, E.: Automatic identification of pro and con reasons in online reviews. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 483–490. Association for Computational Linguistics (2006)Google Scholar
  8. 8.
    Kešelj, V., Peng, F., Cercone, N., Thomas, C.: N-gram-based author profiles for authorship attribution. In: Proceedings of the Conference Pacific Association for Computational Linguistics, PACLING, vol. 3, pp. 255–264 (2003)Google Scholar
  9. 9.
    Reddy, D.K.S., Pujari, A.K.: N-gram analysis for computer virus detection. J. Comput. Virol. 2(3), 231–239 (2006)CrossRefGoogle Scholar
  10. 10.
    Wang, X., McCallum, A., Wei, X.: Topical N-grams: phrase and topic discovery, with an application to information retrieval. In: Seventh IEEE International Conference on ICDM, pp.697–702 (2007)Google Scholar
  11. 11.
    Ikeda, D., Takamura, H., Okumura, M.: Semi-supervised learning for blog classification. In: AAAI, pp. 1156–1161 (2008)Google Scholar
  12. 12.
    HaCohen-Kerner, Y., Rosenfeld, A., Tzidkani, M., Cohen, D.N.: Classifying papers from different computer science conferences. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013, Part I. LNCS, vol. 8346, pp. 529–541. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    HaCohen-Kerner, Y., Beck, H., Yehudai, E., Mughaz, D.: Stylistic feature sets as classifiers of documents according to their historical period and ethnic origin. Appl. Artif. Intell. 24(9), 847–862 (2010)CrossRefzbMATHGoogle Scholar
  14. 14.
    HaCohen-Kerner, Y., Beck, H., Yehudai, E., Rosenstein, M., Mughaz, D.: Cuisine: classification using stylistic feature sets and/or name-based feature sets. J. Am. Soc. Inf. Sci. Technol. 61(8), 1644–1657 (2010)Google Scholar
  15. 15.
    Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22(2), 110–125 (2006)Google Scholar
  16. 16.
    Gamon, M., Basu, S., Belenko, D., Fisher, D., Hurst, M., König, A.C.: BLEWS: using blogs to provide context for news articles. In: Proceedings of the Second International AAAI Conference on Weblogs and Social Media (ICWSM), Seattle, Washington, 30 March–2 April 2008‏Google Scholar
  17. 17.
    Bandari, R., Asur, S., Huberman, B.A.: The pulse of news in social media: forecasting popularity. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM) (Arxiv preprint arXiv), Dublin, vol. 1202, pp. 26–33, 4–7 June 2012Google Scholar
  18. 18.
    Swezey, R.M.E., Sano, H., Shiramatsu, S., Ozono, T., Shintani, T.: Automatic detection of news articles of interest to regional communities. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 12(6), 99–106 (2012)Google Scholar
  19. 19.
    Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1555–1565 (2014)Google Scholar
  20. 20.
    Shin, C., Doermann, D., Rosenfeld, A.: Classification of document pages using structure-based features. Int. J. Doc. Anal. Recogn. 3(4), 232–247 (2001)CrossRefGoogle Scholar
  21. 21.
    Chen, N., Shatkay, H., Blostein, D.: Exploring a new space of features for document classification: figure clustering. In: Proceedings of the 2006 Conference of the Center for Advanced Studies on Collaborative research, p. 35. IBM Corp (2006)Google Scholar
  22. 22.
    Liparas, D., HaCohen-Kerner, Y., Moumtzidou, A., Vrochidis, S., Kompatsiaris, I.: News articles classification using random forests and weighted multimodal features. In: Lamas, D., Buitelaar, P. (eds.) IRFC 2014. LNCS, vol. 8849, pp. 63–75. Springer, Heidelberg (2014)Google Scholar
  23. 23.
    Augereau, O., Journet, N., Vialard, A., Domenger, J.P.: Improving classification of an industrial document image database by combining visual and textual features. In: In Proceedings of the 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 314–318. IEEE (2014)Google Scholar
  24. 24.
    Fox, C.: A stop list for general text. ACM SIGIR Forum 24(1–2), 19–35 (1989)CrossRefGoogle Scholar
  25. 25.
    Van De Sande, K.E., Gevers, T., Snoek, C.G.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  26. 26.
    Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3304–3311 (2010)Google Scholar
  27. 27.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  28. 28.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  29. 29.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  30. 30.
    Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods – Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1998)Google Scholar
  31. 31.
    Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to platt’s SMO algorithm for SVM classifier design. Neural Comput. 13(3), 637–649 (2001)CrossRefzbMATHGoogle Scholar
  32. 32.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  33. 33.
    Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand (1998)Google Scholar
  34. 34.
    Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Yaakov HaCohen-Kerner
    • 1
  • Asaf Sabag
    • 1
  • Dimitris Liparas
    • 2
  • Anastasia Moumtzidou
    • 2
  • Stefanos Vrochidis
    • 2
  • Ioannis Kompatsiaris
    • 2
  1. 1.Department of Computer ScienceJerusalem College of Technology - Lev Academic CenterJerusalemIsrael
  2. 2.Centre for Research and Technology Hellas, Information Technologies InstituteThermi, ThessalonikiGreece

Personalised recommendations