Skip to main content
SpringerLink
Log in

Incremetal GEP-Based Ensemble Classifier

  • Conference paper
  • First Online:
Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 72))

Included in the following conference series:

Abstract

In this paper we propose a new incremental Gene Expression Programming (GEP) ensemble classifier. Our base classifiers are induced from a chunk of data instances using GEP. Size of the chunk controls the number of instances with known class labels used to induce base classifiers iteratively. Instances with unknown class label are classified in sequence, one by one. It is assumed that after a decision as to the class label of the new instance has been taken its true class label is revealed. From a set of base classifier a metagene is induced and used to predict class label of instances with unknown class labels. To validate the approach an extensive computational experiment has been carried-out.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. MOA Analysis: UCI Machine Learning Repository (2013). http://moa.cms.waikato.ac.nz/datasets/

  2. Bertini, J.R.J., Zhao, L., Lopes, A.A.: An incremental learning algorithm based on the k-associated graph for non-stationary data classification. Inf. Sci. 246, 52–68 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  3. Cohen, L., Avrahami, G., Last, M., Kandel, A.: Info-fuzzy algorithms for mining dynamic data streams. Appl. Soft Comput. 8(4), 1283–1294 (2008). http://dx.doi.org/10.1016/j.asoc.2007.11.003

  4. Fern, A., Givan, R.: Online ensemble learning: an empirical study. Mach. Learn. 53(1-2), 71–109 (2003). http://dx.doi.org/10.1023/A:1025619426553

  5. Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. CoRR cs.AI/0102027 (2001). http://arxiv.org/abs/cs.AI/0102027

  6. Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. SCI, vol. 21. Springer, Heidelberg (2006)

    Google Scholar 

  7. Hulten, G., Spencer, L., Domingos, P.M.: Mining time-changing data streams. In: Lee, D., Schkolnick, M., Provost, F.J., Srikant, R. (eds.) Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 26–29 August, pp. 97–106. ACM (2001). http://portal.acm.org/citation.cfm?id=502512.502529

  8. Jedrzejowicz, J., Jedrzejowicz, P.: GEP-induced expression trees as weak classifiers. In: Perner, P. (ed.) ICDM 2008. LNCS, vol. 5077, pp. 129–141. Springer, Heidelberg (2008). doi:10.1007/978-3-540-70720-2_10

    Chapter  Google Scholar 

  9. Jedrzejowicz, J., Jedrzejowicz, P.: A family of GEP-induced ensemble classifiers. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 641–652. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04441-0_56

    Chapter  Google Scholar 

  10. Jedrzejowicz, J., Jedrzejowicz, P.: Experimental evaluation of two new gep-based ensemble classifiers. Expert Syst. Appl. 38(9), 10932–10939 (2011). http://dx.doi.org/10.1016/j.eswa.2011.02.135

  11. Jedrzejowicz, J., Jedrzejowicz, P.: Distance-based online classifiers. Expert Syst. Appl. 60, 249–257 (2016). http://dx.doi.org/10.1016/j.eswa.2016.05.015

  12. Kotsiantis, S.B.: An incremental ensemble of classifiers. Artif. Intell. Rev. 36(4), 249–266 (2011). http://dx.doi.org/10.1007/s10462-011-9211-4

  13. Lichman, M.: UCI Machine Learning Repository (2013). http://archive.ics.uci.edu/ml/

  14. Liu, D., Wu, Y., Jiang, H.: FP-ELM: an online sequential learning algorithm for dealing with concept drift. Neurocomputing 207, 322–334 (2016). http://www.sciencedirect.com/science/article/pii/S0925231216303125

  15. Mldata.org: Machine learning data set repository (2013). http://mldata.org/repository/tags/data/

  16. Moreno-Torres, J.G., Sáez, J.A., Herrera, F.: Study on the impact of partition-induced dataset shift on k -fold cross-validation. IEEE Trans. Neural Netw. Learning Syst. 23(8), 1304–1312 (2012). http://dx.doi.org/10.1109/TNNLS.2012.2199516

  17. Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Mach. Learn. 1(3), 317–354 (1986). http://dx.doi.org/10.1023/A:1022810614389

  18. Todorovski, L., Dzeroski, S.: Combining classifiers with meta decision trees. Mach. Learn. 50(3), 223–249 (2003). http://dx.doi.org/10.1023/A:1021709817809

  19. Torres, D.M., Aguilar-Ruiz, J.S.: A similarity-based approach for data stream classification. Expert Syst. Appl. 41(9), 4224–4234 (2014). http://dx.doi.org/10.1016/j.eswa.2013.12.041

  20. Turkov, P., Krasotkina, O., Mottl, V.: Dynamic programming for bayesian logistic regression learning under concept drift. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) PReMI 2013. LNCS, vol. 8251, pp. 190–195. Springer, Heidelberg (2013). doi:10.1007/978-3-642-45062-4_26

    Chapter  Google Scholar 

  21. Utgoff, P.E., Berkman, N.C., Clouse, J.A.: Decision tree induction based on efficient tree restructuring. Mach. Learn. 29(1), 5–44 (1997). http://dx.doi.org/10.1023/A:1007413323501

  22. Wang, L., Ji, H., Jin, Y.: Fuzzy passive-aggressive classification: a robust and efficient algorithm for online classification problems. Inf. Sci. 220, 46–63 (2013)

    Article  Google Scholar 

  23. Wei, X., Huang, H.: Granular twin support vector machines based on mixture kernel function. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS, vol. 9227, pp. 43–54. Springer, Cham (2015). doi:10.1007/978-3-319-22053-6_5

    Chapter  Google Scholar 

  24. Wisaeng, K.: A comparison of different classification techniques for bank direct marketing. Int. J. Soft Comput. Eng. 3(4), 116–119 (2013)

    Google Scholar 

  25. Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992). http://dx.doi.org/10.1016/S0893-6080(05)80023-1

  26. Xu, S., Wang, J.: A fast incremental extreme learning machine algorithm for data streams classification. Expert Syst. Appl. 65(C), 332–344 (2016). https://doi.org/10.1016/j.eswa.2016.08.052

  27. Zliobaite, I.: Combining similarity in time and space for training set formation under concept drift. Intell. Data Anal. 15(4), 589–611 (2011). http://dx.doi.org/10.3233/IDA-2011-0484

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematics, Physics and Informatics, Institute of Informatics, University of Gdansk, 80-308, Gdansk, Poland

    Joanna Jedrzejowicz

  2. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jedrzejowicz

Authors
  1. Joanna Jedrzejowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Jedrzejowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joanna Jedrzejowicz .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Jedrzejowicz, J., Jedrzejowicz, P. (2018). Incremetal GEP-Based Ensemble Classifier. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59421-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59420-0

  • Online ISBN: 978-3-319-59421-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation