Skip to main content
SpringerLink
Log in
Book cover

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing pp 267–277Cite as

The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

Abstract

In the recent works we have investigated the classifiers based on weak rough inclusions, especially the 8v1.1 - 8v1.5 algorithms. These algorithms in process of weights forming for classification dynamically react on the distance between the particular attributes. Our results show the effectiveness of these methods and the wide application in many contexts, especially in the context of classification of DNA Microarray data. In this work we have checked a few methods for classifier stabilisation, such as the Bootstrap Ensemble, Boosting based on Arcing, and Ada-Boost with Monte Carlo split. We have performed experiments on selected data from the UCI Repository. The results show that the committee of weak classifiers stabilised our algorithms in the context of accuracy of classification. The Boosting based on Arcing turned out to be the most promising method among those examined.

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

References

  1. Artiemjew, P.: Classifiers based on rough mereology in analysis of DNA microarray data. In: Proceedings of Second International Conference of Soft Computing and Pattern Recognition (SoCPar2010), pp. 273–278. IEEE Computer Society, Cergy Pontoise (2010)

    Google Scholar 

  2. Artiemjew, P.: The extraction method of DNA microarray features based on experimental A statistics. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 642–648. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Artiemjew, P.: The extraction method of DNA microarray features based on modified F statistics vs. classifier based on rough mereology. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS (LNAI), vol. 6804, pp. 33–42. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Artiemjew, P.: Review of the extraction methods of DNA microarray features based on central decision class separation vs rough set classifier. Found. Comput. Decis. Sci. 37(4), 241–254 (2012)

    MATH  Google Scholar 

  5. Artiemjew, P.: Rough mereology classifier vs simple DNA microarray gene extraction methods. Int. J. Data Min. Model. Manage. Spec. Issue Pattern Recogn. 6(2), 110–126 (2014)

    Google Scholar 

  6. Hájek, P.: Metamathematics of Fuzzy Logic. Kluwer, Dordrecht (1998)

    Book  MATH  Google Scholar 

  7. Polkowski, L.: A unified approach to granulation of knowledge and granular computing based on rough mereology: a survey. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 375–401. Wiley, New York (2008)

    Chapter  Google Scholar 

  8. Polkowski, L., Artiemjew, P.: On classifying mappings induced by granular structures. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 264–286. Springer, Heidelberg (2008)

    Chapter  MATH  Google Scholar 

  9. Polkowski, L., Artiemjew, P.: Granular Computing in Decision Approximation - An Application of Rough Mereology. Intelligent Systems Reference Library, vol. 77. Springer, Switzerland (2015). ISBN 978-3-319-12879-5, pp. 1–422

    Book  MATH  Google Scholar 

  10. Ohno-Machado, L.: Cross-validation and Bootstrap Ensembles, Bagging, Boosting, Harvard-MIT Division of Health Sciences and Technology, HST.951J: Medical Decision Support, Fall (2005). http://ocw.mit.edu/courses/health-sciences-and-technology/hst-951j-medical-decision-support-fall-2005/lecture-notes/hst951_6.pdf

  11. Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, p. 23. ISBN 978-1439830031. The term boosting refers to a family of algorithms that are able to convert weak learners to strong learners (2012)

    Google Scholar 

  12. Schapire, R.E.: The boosting approach to machine learning: an overview. In: MSRI (Mathematical Sciences Research Institute) Workshop on Nonlinear Estimation and Classification (2003)

    Google Scholar 

  13. Zhou, Z.-H.: Boosting 25 years, CCL 2014 Keynote (2014)

    Google Scholar 

  14. Breiman, L.: Arcing classifier (with discussion and a rejoinder by the author). Ann. Statist. 26(3), 801–849 (1998). Accessed 18 January 2015. Schapire (1990) proved that boosting is possible, p. 823

    Article  MathSciNet  MATH  Google Scholar 

  15. Schapire, R.E.: A Short Introduction to Boosting (1999)

    Google Scholar 

  16. Hu, X.: Construction of an ensemble of classifiers based on rough sets theory and database operations. In: Proceedings of the IEEE International Conference on Data Mining (ICDM2001) (2001)

    Google Scholar 

  17. Hu, X.: Ensembles of classifiers based on rough sets theory and set-oriented database operations. In: Presented at the 2006 IEEE International Conference on Granular Computing, Atlanta, GA (2006)

    Google Scholar 

  18. Saha, S., Murthy, C.A., Pal, S.K.: Rough set based ensemble classifier for web page classification. Fundamenta Informaticae 76(1–2), 171–187 (2007)

    MathSciNet  Google Scholar 

  19. Shi, L., Weng, M., Ma, X., Xi, L.: Rough set based decision tree ensemble algorithm for text classification. J. Comput. Inf. Syst. 6(1), 89–95 (2010)

    Google Scholar 

  20. Murthy, C.A., Saha, S., Pal, S.K.: Rough set based ensemble classifier. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 27–27. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

Download references

Acknowledgements

The research has been supported by grant 1309-802 from Ministry of Science and Higher Education of the Republic of Poland.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

Authors
  1. Piotr Artiemjew
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Piotr Artiemjew .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Artiemjew, P. (2015). The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25783-9_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation