Skip to main content
SpringerLink
Log in

Using \(\varphi -\delta \) Diagrams on Web Data

  • Conference paper
  • First Online:
Current Trends in Web Engineering (ICWE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11153))

Included in the following conference series:

  • 754 Accesses

Abstract

As the Web is still expanding, also the demand for fast and accurate tools aimed at analyzing digital documents (e.g., webpages) is constantly growing. In this scenario, the main strategies for producing accurate predictive models are mostly focused on the assessment of classifiers performances and features. In this work, a graphical tool, called \( \varphi - \delta \) diagrams, is applied to some use cases aimed at highlighting its potential in supporting development and implementation of Web systems and services. In particular, \( \varphi - \delta \) diagrams permit to visualize (i) classifier performance, in terms of accuracy and bias, and (ii) variable importance, useful to define feature ranking, selection or reduction algorithms. The proposed use cases emphasize the usefulness of the tool when dealing with Web data.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    http://opendirectoryproject.org/.

References

  1. Armano, G.: A direct measure of discriminant and characteristic capability for classifier building and assessment. Inf. Sci. 325, 466–483 (2015)

    Article  Google Scholar 

  2. Bellman, R.: Adaptive Control Processes. Princeton University Press, Princeton (1961)

    Book  Google Scholar 

  3. Bradley, A.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30, 1145–1159 (1997)

    Article  Google Scholar 

  4. Brier, G.W.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78(1), 1–3 (1950)

    Article  Google Scholar 

  5. Cano, A., Zafra, A., Ventura, S.: Speeding up multiple instance learning classification rules on GPUs. Knowl. Inf. Syst. 44(1), 127–145 (2015). https://doi.org/10.1007/s10115-014-0752-0

    Article  Google Scholar 

  6. Cramer, H.: Mathematical Methods of Statistics/by Harald Cramer. Princeton University Press, Princeton (1946)

    Google Scholar 

  7. Elazmeh, W., Japkowicz, N., Matwin, S.: A framework for comparative evaluation of classifiers in the presence of class imbalance, p. 25 (2006)

    Google Scholar 

  8. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. (Special issue: ROC analysis in pattern recognition) 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  9. Fürnkranz, J., Flach, P.A.: Roc ‘n’ rule learning - towards a better understanding of covering algorithms. Mach. Learn. 58(1), 39–77 (2005)

    Article  Google Scholar 

  10. Good, I.J.: Rational decisions. J. R. Stat. Soc. (Series B) 14, 107–114 (1952)

    MathSciNet  Google Scholar 

  11. Guo, X., Yin, Y., Dong, C., Yang, G., Zhou, G.: On the class imbalance problem. In: Proceedings of the 4th International Conference on Natural Computation, ICNC 2008. IEEE (2008)

    Google Scholar 

  12. Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17, 299–310 (2005)

    Article  Google Scholar 

  13. Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)

    Google Scholar 

  14. Kalinov, P., Stantic, B., Sattar, A.: Building a dynamic classifier for large text data collections. In: Proceedings of the Twenty-First Australasian Database Conference on Database Technologies, (ADC 2010), Brisbane, 18–22 January 2010, pp. 113–122 (2010)

    Google Scholar 

  15. Kenekayoro, P., Buckley, K., Thelwall, M.: Automatic classification of academic web page types. Scientometrics 101(2), 1015–1026 (2014). https://doi.org/10.1007/s11192-014-1292-9

    Article  Google Scholar 

  16. Mohammad, R.M., Thabtah, F.A., McCluskey, L.: Predicting phishing websites based on self-structuring neural network. Neural Comput. Appl. 25(2), 443–458 (2014)

    Article  Google Scholar 

  17. Pearson, K.: VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 187, 253–318 (1896). http://rsta.royalsocietypublishing.org/content/187/253

    Article  Google Scholar 

  18. Qi, X., Davison, B.D.: Web page classification: features and algorithms. ACM Comput. Surv. 41(2), 12:1–12:31 (2009). https://doi.org/10.1145/1459352.1459357

    Article  Google Scholar 

  19. Schonhofen, P.: Identifying document topics using the Wikipedia category network. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2006, pp. 456–462. IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  20. Zhu, J., Xie, Q., Yu, S.I., Wong, W.H.: Exploiting link structure for web page genre identification. Data Min. Knowl. Discov. http://hdl.handle.net/10754/566107

  21. Zipf, G.K.: Human Behaviour and the Principle of Least Effort. Addison-Wesley, Cambridge (1949)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

    Giuliano Armano & Alessandro Giuliani

Authors
  1. Giuliano Armano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessandro Giuliani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuliano Armano .

Editor information

Editors and Affiliations

  1. Faculty of Informatics, University of Lugano, Lugano, Switzerland

    Cesare Pautasso

  2. Quercus Software Engineering Group, Universidad de Extremadura, Cáceres, Spain

    Fernando Sánchez-Figueroa

  3. Tampere University of Technology, Tampere, Finland

    Kari Systä

  4. Escuela Politécnica, Universidad de Extremadura, Cáceres, Spain

    Juan Manuel Murillo Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Armano, G., Giuliani, A. (2018). Using \(\varphi -\delta \) Diagrams on Web Data. In: Pautasso, C., Sánchez-Figueroa, F., Systä, K., Murillo Rodríguez, J. (eds) Current Trends in Web Engineering. ICWE 2018. Lecture Notes in Computer Science(), vol 11153. Springer, Cham. https://doi.org/10.1007/978-3-030-03056-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03056-8_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03055-1

  • Online ISBN: 978-3-030-03056-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation