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On Robustness of Adaptive Random Forest Classifier on Biomedical Data Stream

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Intelligent Information and Database Systems (ACIIDS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12033))

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Abstract

Data Stream represents a significant challenge for data analysis and data mining techniques because those techniques are developed based on training batch data. Classification technique that deals with data stream should have the ability for adapting its model for the new samples and forget the old ones. In this paper, we present an intensive comparison for the performance of six of popular classification techniques and focusing on the power of Adaptive Random Forest. The comparison was made based on four real medical datasets and for more reliable results, 40 other datasets were made by adding white noise to the original datasets. The experimental results showed the dominant of Adaptive Random Forest over five other techniques with high robustness against the change in data and noise.

A. Kiss was also with J. Selye University, Komarno, Slovakia.

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References

  1. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106. ACM (2001)

    Google Scholar 

  2. Gantz, J., Reinsel, D.: The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future 2007(2012), 1–16 (2012)

    Google Scholar 

  3. Gama, J.: Knowledge Discovery from Data Streams. Chapman and Hall/CRC, London (2010)

    Book  Google Scholar 

  4. Krempl, G., et al.: Open challenges for data stream mining research. ACM SIGKDD Explor. Newsl. 16(1), 1–10 (2014)

    Article  Google Scholar 

  5. Babenko, B., Yang, M.-H., Belongie, S.: A family of online boosting algorithms. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1346–1353. IEEE (2009)

    Google Scholar 

  6. Bifet, A., Holmes, G., Pfahringer, B., Gavaldà, R.: Improving adaptive bagging methods for evolving data streams. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS (LNAI), vol. 5828, pp. 23–37. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-05224-8_4

    Chapter  Google Scholar 

  7. Fong, S., et al.: Stream-based biomedical classification algorithms for analyzing biosignals. J. Inf. Process. Syst. 7(4), 717–732 (2011)

    Article  Google Scholar 

  8. Hang, Y., et al.: Case-based and stream-based classification in biomedical application. In: Eighth IASTED International Conference on Biomedical Engineering (Biomed 2011), pp. 207–214. February 2011

    Google Scholar 

  9. Zhang, Y., et al.: Real-time clinical decision support system with data stream mining. In: BioMed Research International 2012 (2012)

    Google Scholar 

  10. Cazzolato, M.T., Ribeiro, M.X.: A statistical decision tree algorithm for medical data stream mining. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 389–392. IEEE (2013)

    Google Scholar 

  11. Zhu, M., et al.: Class weights random forest algorithm for processing class imbalanced medical data. IEEE Access 6, 4641–4652 (2018)

    Article  Google Scholar 

  12. Al-Shammari, A., Zhou, R., Liu, C., Naseriparsa, M., Vo, B.Q.: A framework for processing cumulative frequency queries over medical data streams. In: Hacid, H., Cellary, W., Wang, H., Paik, H.-Y., Zhou, R. (eds.) WISE 2018. LNCS, vol. 11234, pp. 121–131. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02925-8_9

    Chapter  Google Scholar 

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

    Google Scholar 

  14. Losing, V., Hammer, B., Wersing, H.: KNN classifier with self adjusting memory for heterogeneous concept drift. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300. IEEE (2016)

    Google Scholar 

  15. Salperwyck, C., Lemaire, V., Hue, C.: Incremental weighted naive bays classifiers for data stream. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds.) Data Science, Learning by Latent Structures, and Knowledge Discovery. SCDAKO, pp. 179–190. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-44983-7_16

    Chapter  Google Scholar 

  16. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Kdd. vol. 2, p. 4 (2000)

    Google Scholar 

  17. Irvine UC: Machine Learning Repository. July 2019. url: https://archive.ics.uci.edu/ml/index.php

  18. kaggle Rebosotiry: Public Datasets. July 2019. url: https://www.kaggle.com/datasets

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Acknowledgment

The project was supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002).

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Authors and Affiliations

  1. Faculty of Informatics, Department of Information Systems, Eötvös Loránd University, Budapest, Hungary

    Hayder K. Fatlawi & Attila Kiss

  2. Center of Information Technology Research and Development, University of Kufa, Najaf, Iraq

    Hayder K. Fatlawi

Authors
  1. Hayder K. Fatlawi
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  2. Attila Kiss
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Corresponding author

Correspondence to Attila Kiss .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

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Fatlawi, H.K., Kiss, A. (2020). On Robustness of Adaptive Random Forest Classifier on Biomedical Data Stream. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-41964-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41963-9

  • Online ISBN: 978-3-030-41964-6

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