Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Hardware Supported Rule-Based Classification on Big Datasets

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Included in the following conference series:

Abstract

In this paper we propose a combination of capabilities of the Field Programmable Gate Arrays based device and PC computer for data processing resulting in classification using previously generated decision rules. Solution is focused on big datasets. Presented architecture has been tested in programmable unit on real datasets. Obtained results confirm the significant acceleration of the computation time using hardware supported operations in comparison to software implementation.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  1. Grześ, T., Kopczyński, M., Stepaniuk, J.: FPGA in rough set based core and reduct computation. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 263–270. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41299-8_25

    Chapter  Google Scholar 

  2. Grzymala-Busse, J.W.: Rule Induction, Data Mining and Knowledge Discovery Handbook, pp. 249-265. Springer, New York (2010)

    Google Scholar 

  3. Kanasugi, A., Yokoyama, A.: A basic design for rough set processor. In: The 15th Annual Conference of Japanese Society for Artificial Intelligence (2001)

    Google Scholar 

  4. Kopczyński, M., Stepaniuk, J.: Rough sets and intelligent systems - professor Zdzisław Pawlak in memoriam, intelligent systems reference library. In: Skowron, A., Suraj, Z. (eds.) Hardware Implementations of Rough Set Methods in Programmable Logic Devices, pp. 309–321. Springer, Heidelberg (2013)

    Google Scholar 

  5. Kopczyński, M., Grześ, T., Stepaniuk, J.: FPGA in rough-granular computing : reduct generation. In: The 2014 IEEE/WCI/ACM International Joint Conferences on Web Intelligence, WI 2014, vol. 2, pp. 364–370. IEEE Computer Society, Warsaw (2014)

    Google Scholar 

  6. Kopczynski, M., Grzes, T., Stepaniuk, J.: Generating core in rough set theory: design and implementation on FPGA. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 209–216. Springer, Cham (2014). doi:10.1007/978-3-319-08729-0_20

    Chapter  Google Scholar 

  7. Kopczyński, M., Grześ, T., Stepaniuk, J.: Core for large datasets: rough sets on FPGA. Fundam. Inform. 147, 241–259 (2016)

    Article  MathSciNet  Google Scholar 

  8. Kopczyński, M., Grześ, T., Stepaniuk, J.: Rough sets based LEM2 rules generation supported by FPGA. Fundam. Inform. 148, 107–121 (2016)

    Article  MathSciNet  Google Scholar 

  9. Kopczynski, M., Grzes, T., Stepaniuk, J.: Hardware supported rough sets based rules generation for big datasets. In: Saeed, K., Homenda, W. (eds.) CISIM 2016. LNCS, vol. 9842, pp. 91–102. Springer, Cham (2016). doi:10.1007/978-3-319-45378-1_9

    Chapter  Google Scholar 

  10. Lewis, T., Perkowski, M., Jozwiak, L.: Learning in hardware: architecture and implementation of an FPGA-based rough set machine. In: 25th EUROMICRO Conference (EUROMICRO 1999), euromicro, vol. 1, p. 1326 (1999)

    Google Scholar 

  11. Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013). http://archive.ics.uci.edu/ml

  12. Muraszkiewicz, M., Rybinski, H.: Towards a parallel rough sets computer. In: Ziarko, W.P. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery, pp. 434–443. Springer, London (1994)

    Chapter  Google Scholar 

  13. Pawlak, Z.: Elementary rough set granules: toward a rough set processor. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neurocomputing: Techniques for Computing with Words, Cognitive Technologies, pp. 5–14. Springer, Berlin (2004)

    Google Scholar 

  14. Snijders, C., Matzat, U., Reips, U.-D.: Big data: big gaps of knowledge in the field of internet science. Int. J. Internet Sci. 7, 1–5 (2012)

    Google Scholar 

  15. Stepaniuk, J.: Knowledge discovery by application of rough set models. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, New Developments in Knowledge Discovery in Information Systems, pp. 137–233. Physica-Verlag, Heidelberg (2000)

    Google Scholar 

  16. Stepaniuk, J.: Rough-Granular Computing in Knowledge Discovery and Data Mining. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  17. Stepaniuk, J., Kopczyński, M., Grześ, T.: The first step toward processor for rough set methods. Fundam. Inform. 127, 429–443 (2013)

    Google Scholar 

Download references

Acknowledgements

The present study was supported by a grant S/WI/3/2013 from Bialystok University of Technology and founded from the resources for research by Ministry of Science and Higher Education.

Author information

Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351, Bialystok, Poland

    Maciej Kopczynski, Tomasz Grzes & Jaroslaw Stepaniuk

Authors
  1. Maciej Kopczynski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomasz Grzes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jaroslaw Stepaniuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaroslaw Stepaniuk .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kopczynski, M., Grzes, T., Stepaniuk, J. (2017). Hardware Supported Rule-Based Classification on Big Datasets. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60837-2_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation