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Random Projection in the Presence of Concept Drift in Supervised Environments

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12415))

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Abstract

In static environments Random Projection (RP) is a popular and efficient technique to preprocess high-dimensional data and to reduce its dimensionality. While RP has been widely used and evaluated in stationary data analysis scenarios, non-stationary environments are not well analyzed. In this paper we provide an evaluation of RP on streaming data including a concept of altering dimensions. We discuss why RP can be used in this scenario and how it can handle stream specific situations like concept drift. We also provide experiments with RP on streaming data, using state-of-the-art streaming classifiers like Adaptive Hoeffding Tree and concept drift detectors on streams containing altering dimensions.

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Notes

  1. 1.

    All experiments are implemented in Python supported by the scikit-multiflow framework [18].

  2. 2.

    https://github.com/ChristophRaab/stvm, we are using ‘org vs people’.

References

  1. Achlioptas, D.: Database-friendly random projections. In: Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 274–281. ACM (2001)

    Google Scholar 

  2. Achlioptas, D.: Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66, 671–687 (2003)

    Article  MathSciNet  Google Scholar 

  3. Aggarwal, C.C.: A survey of stream classification algorithms. In: Data Classification: Algorithms and Applications (2014)

    Google Scholar 

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

    Google Scholar 

  5. Bifet, A., Gavaldà, R.: Adaptive learning from evolving data streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 249–260. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03915-7_22

    Chapter  Google Scholar 

  6. Carraher, L.A., Wilsey, P.A., Moitra, A., Dey, S.: Random projection clustering on streaming data. In: 2016 IEEE 16th ICDMW, pp. 708–715 (2016)

    Google Scholar 

  7. Dasgupta, S., Gupta, A.: An elementary proof of a theorem of Johnson and Lindenstrauss. Random Struct. Algorithms 22, 60–65 (2003)

    Article  MathSciNet  Google Scholar 

  8. Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 1–37 (2014)

    Article  Google Scholar 

  9. Gomes, H.M., et al.: Adaptive random forests for evolving data stream classification. Mach. Learn. 106(9), 1469–1495 (2017). https://doi.org/10.1007/s10994-017-5642-8

    Article  MathSciNet  MATH  Google Scholar 

  10. Grabowska, M., Kotłowski, W.: Online principal component analysis for evolving data streams. In: Czachórski, T., Gelenbe, E., Grochla, K., Lent, R. (eds.) ISCIS 2018. CCIS, vol. 935, pp. 130–137. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00840-6_15

    Chapter  Google Scholar 

  11. Heusinger, M., Raab, C., Schleif, F.-M.: Passive concept drift handling via momentum based robust soft learning vector quantization. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J.D. (eds.) WSOM 2019. AISC, vol. 976, pp. 200–209. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-19642-4_20

    Chapter  Google Scholar 

  12. Johnson, W.B., Lindenstrauss, J.: Extensions of Lipschitz mappings into a Hilbert space. Contemp. Math. 26, 189–206 (1984)

    Article  MathSciNet  Google Scholar 

  13. Kaban, A.: Improved bounds on the dot product under random projection and random sign projection. In: Proceedings of the 21th ACM SIGKDD. KDD 2015, pp. 487–496. ACM, New York (2015)

    Google Scholar 

  14. Klartag, B., Mendelson, S.: Empirical processes and random projections. J. Funct. Anal. 225(1), 229–245 (2005)

    Article  MathSciNet  Google Scholar 

  15. Li, P., Hastie, T.J., Church, K.W.: Very sparse random projections. In: Proceedings of the 12th ACM SIGKDD, pp. 287–296. ACM (2006)

    Google Scholar 

  16. Losing, V., Hammer, B., Wersing, H.: KNN classifier with self adjusting memory for heterogeneous concept drift. In: Proceedings of the - IEEE, ICDM, pp. 291–300 (2017)

    Google Scholar 

  17. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  18. Montiel, J., Read, J., Bifet, A., Abdessalem, T.: Scikit-multiflow: a multi-output streaming framework. J. Mach. Learn. Res. 19(72), 1–5 (2018)

    MATH  Google Scholar 

  19. Oza, N.C.: Online bagging and boosting. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2340–2345 (2005)

    Google Scholar 

  20. Pham, X.C., Dang, M.T., Dinh, S.V., Hoang, S., Nguyen, T.T., Liew, A.W.: Learning from data stream based on random projection and Hoeffding tree classifier. In: DICTA 2017, pp. 1–8 (2017)

    Google Scholar 

  21. Raab, C., Heusinger, M., Schleif, F.M.: Reactive soft prototype computing for frequent reoccurring concept drift. In: Proceedings of the 27. ESANN, pp. 437–442 (2019)

    Google Scholar 

  22. Sacha, D., et al.: Visual interaction with dimensionality reduction: a structured literature analysis. IEEE Trans. Vis. Comput. Graph. 23(1), 241–250 (2017)

    Article  MathSciNet  Google Scholar 

  23. Schoeneman, F., Mahapatra, S., Chandola, V., Napp, N., Zola, J.: Error metrics for learning reliable manifolds from streaming data. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 750–758. SIAM (2017)

    Google Scholar 

  24. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2, 37–52 (1987)

    Article  Google Scholar 

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Acknowledgement

We are thankful for support in the FuE program Informations- und Kommunikationstechnik of the StMWi, project OBerA, grant number IUK-1709-0011// IUK530/010.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Applied Sciences Würzburg-Schweinfurt, Sanderheinrichsleitenweg 20, Würzburg, Germany

    Moritz Heusinger

  2. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Frank-Michael Schleif

Authors
  1. Moritz Heusinger
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  2. Frank-Michael Schleif
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Corresponding author

Correspondence to Moritz Heusinger .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Heusinger, M., Schleif, FM. (2020). Random Projection in the Presence of Concept Drift in Supervised Environments. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_48

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  • DOI: https://doi.org/10.1007/978-3-030-61401-0_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61400-3

  • Online ISBN: 978-3-030-61401-0

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