Skip to main content
SpringerLink
Log in

Advances in Feature Selection for Data and Pattern Recognition: An Introduction

  • Chapter
  • First Online:
Advances in Feature Selection for Data and Pattern Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 138))

Abstract

Technological progress of the ever evolving world is connected with the need of developing methods for extracting knowledge from available data, distinguishing variables that are relevant from irrelevant, and reduction of dimensionality by selection of the most informative and important descriptors. As a result, the field of feature selection for data and pattern recognition is studied with such unceasing intensity by researchers, that it is not possible to present all facets of their investigations. The aim of this chapter is to provide a brief overview of some recent advances in the domain, presented as chapters included in this monograph.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Abraham, A., FalcĂłn, R., Bello, R. (eds.): Rough Set Theory: A True Landmark in Data Analysis, Studies in Computational Intelligence, vol. 174. Springer, Heidelberg (2009)

    Google Scholar 

  2. Ahmad, M., Peters, J.: Delta complexes in digital images. Approximating image object shapes, 1–18 (2017). arXiv:170604549v1

  3. Bertini Jr., J.R., Nicoletti, M.C., Zhao, L.: An embedded imputation method via attribute-based decision graphs. Expert Syst. Appl. 57(C), 159–177 (2016)

    Google Scholar 

  4. Bi, W., Kwok, J.: Multi-label classification on tree- and dag-structured hierarchies. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 17–24. ACM, New York, NY, USA (2011)

    Google Scholar 

  5. BƂaszczyƄski, J., Stefanowski, J.: Actively balanced bagging for imbalanced data. In: Kryszkiewicz, M., Appice, A., ƚlÈ©zak, D., RybiƄski, H., Skowron, A., Raƛ, Z.W. (eds.) Foundations of Intelligent Systems - 23rd International Symposium, ISMIS 2017, Warsaw, Poland, June 26–29, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10352, pp. 271–281. Springer, Cham (2017)

    Google Scholar 

  6. Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. 49(2), 31:1–31:50 (2016)

    Article  Google Scholar 

  7. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  8. Deuntsch, I., Gediga, G.: Rough set data analysis: a road to noninvasive knowledge discovery. Matho\(\delta \)os Publishers, Bangor (2000)

    Google Scholar 

  9. Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evolut. Comput. 1(1), 53–66 (1997)

    Google Scholar 

  10. Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8(1), 87–102 (1992)

    MATH  Google Scholar 

  11. Fiesler, E., Beale, R.: Handbook of Neural Computation. Oxford University Press, Oxford (1997)

    Google Scholar 

  12. Goodwin, S., Dykes, J., Slingsby, A., Turkay, C.: Visualizing multiple variables across scale and geography. IEEE Trans. Visual Comput. Graphics 22(1), 599–608 (2016)

    Article  Google Scholar 

  13. GrzymaƂa-Busse, J.W.: Data reduction: discretization of numerical attributes. In: Klösgen, W., Zytkow, J.M. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 218–225. Oxford University Press Inc., New York (2002)

    Google Scholar 

  14. Guyon, I.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  15. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.): Feature Extraction: Foundations and Applications. Springer, Heidelberg (2006)

    Google Scholar 

  16. Guyon, I., Gunn, S.R., Asa, B., Dror, G.: Result analysis of the NIPS 2003 feature selection challenge. In: Proceedings of the 17th International Conference on Neural Information Processing Systems, pp. 545–552 (2004)

    Google Scholar 

  17. Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection. Wiley, Hoboken (2008)

    Book  Google Scholar 

  18. Kharrat, A., Halima, M.B., Ayed, M.B.: MRI brain tumor classification using support vector machines and meta-heuristic method. In: 15th International Conference on Intelligent Systems Design and Applications, ISDA 2015, Marrakech, Morocco, December 14–16, 2015, pp. 446–451. IEEE (2015)

    Google Scholar 

  19. Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman & Hall/CRC, Boca Raton (2008)

    MATH  Google Scholar 

  20. MeszlĂ©nyi, R., Peska, L., GĂĄl, V., VidnyĂĄnszky, Z., Buza, K.: Classification of fMRI data using dynamic time warping based functional connectivity analysis. In: 2016 24th European Conference on Signal Processing (EUSIPCO), pp. 245–249. IEEE (2016)

    Google Scholar 

  21. Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach, Studies in Computational Intelligence, vol. 360. Springer, Heidelberg (2011)

    Book  MATH  Google Scholar 

  22. Nowak-BrzeziƄska, A.: Mining rule-based knowledge bases inspired by rough set theory. Fundamenta Informaticae 148, 35–50 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  23. Opelt, A., Pinz, A., Zisserman, A.: Learning an alphabet of shape and appearance for multi-class object detection. Int. J. Comput. Vis. 80(1), 16–44 (2008)

    Article  Google Scholar 

  24. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  MATH  Google Scholar 

  25. PrzybyƂa-Kasperek, M., Wakulicz-Deja, A.: A dispersed decision-making system - the use of negotiations during the dynamic generation of a system’s structure. Inf. Sci. 288(C), 194–219 (2014)

    Google Scholar 

  26. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

    Google Scholar 

  27. Ramanna, S., Singh, A.: Tolerance-based approach to audio signal classification. In: Khoury, R., Drummond, C. (eds.) Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31–June 3, 2016, Proceedings, pp. 83–88. Springer, Cham (2016)

    Google Scholar 

  28. Raƛ, Z.W., Dardzinska, A.: From data to classification rules and actions. Int. J. Intell. Syst. 26(6), 572–590 (2011)

    Article  Google Scholar 

  29. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)

    Article  MATH  Google Scholar 

  30. Rudnicki, W.R., WrzesieƄ, M., Paja, W.: All relevant feature selection methods and applications. In: StaƄczyk, U., Jain, L.C. (eds.) Feature Selection for Data and Pattern Recognition, pp. 11–28. Springer, Heidelberg (2015)

    Google Scholar 

  31. StaƄczyk, U.: Selection of decision rules based on attribute ranking. J. Intell. Fuzzy Syst. 29(2), 899–915 (2015)

    Article  MathSciNet  Google Scholar 

  32. StaƄczyk, U.: Weighting and pruning of decision rules by attributes and attribute rankings. In: Czachórski, T.., Gelenbe, E.., Grochla, K., Lent, R. (eds.) Proceedings of the 31st International Symposium on Computer and Information Sciences, Communications in Computer and Information Science, vol. 659, pp. 106–114. Springer, Cracow (2016)

    Google Scholar 

  33. StaƄczyk, U., Jain, L. (eds.): Feature Selection for Data and Pattern Recognition, Studies in Computational Intelligence, vol. 584. Springer, Heidelberg (2015)

    Google Scholar 

  34. Szczuko, P.: Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications. Multimed. Tools Appl. (2017)

    Google Scholar 

  35. Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M.: Brainstorm: a user-friendly application for MEG/EEG analysis. Intell. Neuroscience 2011(8), 8:1–8:13 (2011)

    Google Scholar 

  36. Utracki, J.: Building management system—artificial intelligence elements in ambient living driving and ant programming for energy saving—alternative approach. In: PiÈ©tka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) 5th International Conference on Information Technologies in Medicine, ITIB 2016 KamieƄ ƚla̧ski, Poland, June 20–22, 2016 Proceedings, vol. 2, pp. 109–120. Springer, Cham (2016)

    Google Scholar 

  37. Wolski, M.: Toward foundations of near sets: (pre-)sheaf theoretic approach. Math. Comput. Sci. 7(1), 125–136 (2013)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Urszula StaƄczyk

  2. Institute of Computer Science, University of Silesia in Katowice, BÈ©dziƄska 39, 41-200, Sosnowiec, Poland

    Beata Zielosko

  3. University of Canberra, Canberra, Australia

    Lakhmi C. Jain

Authors
  1. Urszula StaƄczyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beata Zielosko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lakhmi C. Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Urszula StaƄczyk .

Editor information

Editors and Affiliations

  1. Silesian University of Technology , Gliwice, Poland

    Urszula StaƄczyk

  2. University of Silesia in Katowice , Katowice, Poland

    Beata Zielosko

  3. University of Bournemouth , Poole, United Kingdom

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

StaƄczyk, U., Zielosko, B., Jain, L.C. (2018). Advances in Feature Selection for Data and Pattern Recognition: An Introduction. In: StaƄczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67588-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67587-9

  • Online ISBN: 978-3-319-67588-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation