Skip to main content
SpringerLink
Log in

Feature Selection in Texts

  • Conference paper
  • First Online:
Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 (CORES 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

Included in the following conference series:

  • 1050 Accesses

Abstract

Feature selection is used in many application areas relevant to expert and intelligent systems, such as machine learning, data mining, cheminformatics and natural language processing. In this study we propose methods for feature selection and features analysis based on Support Vector Machines (SVM) with linear kernels. We explore how these techniques can be used to obtain some interesting information for further exploration of text data. The results provide satisfactory observations which may lead to progress in feature selection field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Blanco, R., Lioma, C.: Graph-based term weighting for information retrieval. Inf. Retr. 15(1), 54–92 (2012). http://dx.doi.org/10.1007/s10791-011-9172-x

    Article  Google Scholar 

  2. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

  3. Chen, R. (ed.): ICICIS 2011, Part II. CCIS, vol. 135. Springer, Heidelberg (2011)

    Google Scholar 

  4. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  5. Garreta, R., Moncecchi, G.: Learning Scikit-learn: Machine Learning in Python. Packt Publishing (2013)

    Google Scholar 

  6. Gaulton, A., Bellis, L.J., Bento, A.P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., Overington, J.P.: Chembl: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40(D1), D1100 (2011). http://dx.doi.org/10.1093/nar/gkr777

    Article  Google Scholar 

  7. Janecek, A., Gansterer, W.N., Demel, M., Ecker, G.: On the relationship between feature selection and classification accuracy. FSDM 4, 90–105 (2008)

    Google Scholar 

  8. Klekota, J., Roth, F.P.: Chemical substructures that enrich for biological activity. Bioinformatics 24(21), 2518–2525 (2008)

    Article  Google Scholar 

  9. 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 

  10. Kramer, S., De Raedt, L., Helma, C.: Molecular feature mining in HIV data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 136–143 (2001)

    Google Scholar 

  11. Lim, T.S., Loh, W.Y., Shih, Y.S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40(3), 203–228 (2000)

    Article  MATH  Google Scholar 

  12. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, Oakland, CA, USA, pp. 281–297 (1967)

    Google Scholar 

  13. Mladenic, D., Grobelnik, M.: Feature selection for unbalanced class distribution and naive Bayes. In: Proceedings of the 16th International Conference on Machine Learning (ICML), pp. 258–267. Morgan Kaufmann Publishers (1999)

    Google Scholar 

  14. Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.P., Smola, A., Song, L., Yu, P., Yan, X., Borgwardt, K.: Near-optimal supervised feature selection among frequent subgraphs, pp. 1076–1087. Max-Planck-Gesellschaft/Society for Industrial and Applied Mathematics, Philadelphia, May 2009

    Google Scholar 

  15. Wale, N., Watson, I.A., Karypis, G.: Comparison of descriptor spaces for chemical compound retrieval and classification. Knowl. Inf. Syst. 14(3), 347–375 (2008)

    Article  Google Scholar 

  16. Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML 1997, pp. 412–420 (1997)

    Google Scholar 

  17. Zhang, Y., Yang, C., Yang, A., Xiong, C., Zhou, X., Zhang, Z.: Feature selection for classification with class-separability strategy and data envelopment analysis. Neurocomputing 166, 172–184 (2015), http://www.sciencedirect.com/science/article/pii/S0925231215004609

Download references

Acknowledgments

This research was partially supported by National Centre of Science (Poland) Grants No. 2016/21/N/ST6/01019.

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Lojasiewicza 6, 30-348, Kraków, Poland

    Magdalena Wiercioch

Authors
  1. Magdalena Wiercioch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Magdalena Wiercioch .

Editor information

Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Wiercioch, M. (2018). Feature Selection in Texts. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59162-9_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59161-2

  • Online ISBN: 978-3-319-59162-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation