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International Conference on Innovative Techniques and Applications of Artificial Intelligence

SGAI 2018: Artificial Intelligence XXXV pp 121–131Cite as

GEP-Based Ensemble Classifier with Drift-Detection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11311))

Abstract

The paper proposes a new ensemble classifier using Gene Expression Programming as the induction engine. The approach aims at predicting unknown class labels for datasets with concept drift. For constructing the proposed ensemble we use the two-level scheme where at the lower level base classifiers are induced and at the upper level, the meta-classifier is produced. The classification process is controlled by the well-known early drift detection mechanism. To validate the approach computational experiment has been carried out. Its results confirmed that the proposed classifier performs well.

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References

  1. Airlines dataset (2017). http://www.kaggle.com/datasets

  2. Analysis, M.O.: UCI machine learning repository (2013). http://moa.cms.waikato.ac.nz/datasets/

  3. Baena-Garća, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavaldà, R., Morales-Bueno, R.: Early drift detection method. In: International Workshop on Knowledge Discovery from Data Streams, pp. 77–86 (2006)

    Google Scholar 

  4. Barros, R.S.M., Santos, S.G.T.C.: A large-scale comparison of concept drift detectors. Inf. Sci. 451–452, 348–370 (2018). https://doi.org/10.1016/j.ins.2018.04.014

    Article  MathSciNet  Google Scholar 

  5. 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  Google Scholar 

  6. Bifet, A., Gavaldà, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the Seventh SIAM International Conference on Data Mining, 26–28 April 2007, Minneapolis, Minnesota, USA, pp. 443–448. SIAM (2007). https://doi.org/10.1137/1.9781611972771.42

    Chapter  Google Scholar 

  7. de Carvalho Santos, S.G.T., de Barros, R.S.M., Júnior, P.M.G.: Optimizing the parameters of drift detection methods using a genetic algorithm. In: 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015, Vietri sul Mare, Italy, 9–11 November 2015, pp. 1077–1084. IEEE Computer Society (2015). https://doi.org/10.1109/ICTAI.2015.153

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

    Article  Google Scholar 

  9. Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

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

    Article  MATH  Google Scholar 

  11. Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. CoRR cs.AI/0102027 (2001)

    Google Scholar 

  12. Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Studies in Computational Intelligence, vol. 21. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32849-1

  13. Frías-Blanco, I., Verdecia-Cabrera, A., Ortiz-Díaz, A., Carvalho, A.: Fast adaptive stacking of ensembles. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, SAC 2016, pp. 929–934. ACM, New York (2016). https://doi.org/10.1145/2851613.2851655

  14. Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28645-5_29

    Chapter  Google Scholar 

  15. 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, August 26–29, 2001, pp. 97–106. ACM (2001). http://portal.acm.org/citation.cfm?id=502512.502529

  16. Jedrzejowicz, J., Jedrzejowicz, P.: Incremetal GEP-based ensemble classifier. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds.) IDT 2017. SIST, vol. 72, pp. 61–70. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59421-7_6

    Chapter  Google Scholar 

  17. Jȩdrzejowicz, J., Jȩdrzejowicz, P.: GEP-Induced Expression Trees as Weak Classifiers. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077, pp. 129–141. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70720-2_10

    Chapter  Google Scholar 

  18. Jȩdrzejowicz, J., Jȩdrzejowicz, P.: A Family of GEP-Induced Ensemble Classifiers. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 641–652. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04441-0_56

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  20. Jȩdrzejowicz, J., Jȩdrzejowicz, P.: Combining expression trees. In: 2013 IEEE International Conference on Cybernetics, CYBCONF 2013, Lausanne, Switzerland, 13–15 June 2013, pp. 80–85. IEEE (2013). https://doi.org/10.1109/CYBConf.2013.6617448. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6599033

  21. Jȩdrzejowicz, J., Jȩdrzejowicz, P.: Distance-based online classifiers. Expert Syst. Appl. 60, 249–257 (2016). https://doi.org/10.1016/j.eswa.2016.05.015

    Article  MATH  Google Scholar 

  22. Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: an ensemble method for drifting concepts. J. Mach. Learn. Res. 8, 2755–2790 (2007)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  24. de Lima Cabral, D.R., de Barros, R.S.M.: Concept drift detection based on Fisher’s Exact test. Inf. Sci. 442–443, 220–234 (2018). https://doi.org/10.1016/j.ins.2018.02.054

    Article  MathSciNet  Google Scholar 

  25. Liu, S., Liu, Z., Sun, J., Liu, L.: Application of synergetic neural network in online writeprint identification. Int. J. Digit. Content Technol. Appl. 5(3), 126–135 (2011)

    Article  Google Scholar 

  26. Nishida, K., Yamauchi, K.: Detecting concept drift using statistical testing. In: Corruble, V., Takeda, M., Suzuki, E. (eds.) DS 2007. LNCS (LNAI), vol. 4755, pp. 264–269. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75488-6_27

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  28. Tanbeer, S.K., Ahmed, C.F., Jeong, B., Lee, Y.: Sliding window-based frequent pattern mining over data streams. Inf. Sci. 179(22), 3843–3865 (2009). https://doi.org/10.1016/j.ins.2009.07.012

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  30. 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

    Article  Google Scholar 

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Author information

Authors and Affiliations

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

    Joanna Jȩdrzejowicz

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

    Piotr Jȩdrzejowicz

Authors
  1. Joanna Jȩdrzejowicz
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  2. Piotr Jȩdrzejowicz
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Corresponding author

Correspondence to Joanna Jȩdrzejowicz .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Jȩdrzejowicz, J., Jȩdrzejowicz, P. (2018). GEP-Based Ensemble Classifier with Drift-Detection. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-04191-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04190-8

  • Online ISBN: 978-3-030-04191-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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