Rough Sets Based LEM2 Rules Generation Supported by FPGA

• Autorzy: Kopczyński M. , Grześ T. , Stepaniuk J.

Identyfikatory

DOI

10.3233/FI-2016-1425

• Konferencja: Rough Set Theory Workshop (RST’2015); (6; 29-06-2015; University of Warsaw )
• Języki publikacji: EN

Abstrakty

EN

In this paper we propose a combination of capabilities of the FPGA based device and PC computer for rough sets based data processing resulting in generating decision rules. Presented architecture has been tested on the exemplary datasets. Obtained results confirm the significant acceleration of the computation time using hardware supporting rough set operations in comparison to software implementation.

Słowa kluczowe

EN

rough sets; field programmable gate array (FPGA); computer software; computer architecture; operator theory

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2016
• Tom: Vol. 148, nr 1/2
• Strony: 107--122
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr.

Twórcy

autor

Kopczyński M.

• Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351 Białystok, Poland

autor

Grześ T.

• Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351 Białystok, Poland

autor

Stepaniuk J.

• Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351 Białystok, Poland

Bibliografia

• [1] Grześ T, Kopczyński M, Stepaniuk J. FPGA in rough set based core and reduct computation, Lecture Notes in Computer Science Vol.8171: Lecture Notes in Artificial Intelligence, Rough sets and knowledge technology: 8th International Conference: RSKT2013, eds. Pawan Lingras, Marcin Wolski, Chris Cornelis, Sushmita Mitra, Piotr Wasilewski, Berlin, Springer-Verlag, 2013, pp. 263-270. doi: 10.1007/978-3-642-41299-8_25.
• [2] Grzymala-Busse JW. Rule induction, Data Mining and Knowledge Discovery Handbook, eds. Oded Maimom, Lior Rokach, Springer US, 2010, pp. 249-265. ISBN: 978-0-387-09822-7. doi:10.1007/978-0-387-09823-4.
• [3] Kanasugi A, Yokoyama A. A basic design for rough set processor, The 15th Annual Conference of Japanese Society for Artificial Intelligence, 2001. doi: 10.11517/pjsai.JSAI01.0.65.0.
• [4] Kopczyński M, Stepaniuk J. Rough set methods and hardware implementations, Zeszyty Naukowe Politechniki Białostockiej. Informatyka Zeszyt 8, 2011, pp. 5–18.
• [5] Kopczyński M, Stepaniuk J. Hardware Implementations of Rough Set Methods in Programmable Logic Devices, Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam, eds. Andrzej Skowron, Zbigniew Suraj, Intelligent Systems Reference Library 43, Heidelberg, Springer, 2013, pp.309–321. ISBN: 978-3-642-30340-1, 978-3-642-30341-8. doi: 10.1007/978-3-642-30341-8_16.
• [6] Kopczyński M, Grześ T, Stepaniuk J. FPGA in Rough-Granular Computing : Reduct Generation, WI 2014 : The 2014 IEEE/WCI/ACM International Joint Conferences on Web Intelligence Vol.2, eds. Andrzej Skowron, Lipika Dey, Adam Krasuski, Yuefeng Li, Warsaw, IEEE Computer Society, 2014, pp. 364-370. doi: 10.1109/WI-IAT.2014.120.
• [7] Kopczyński M, Grześ T, Stepaniuk J. Generating core in rough set theory : Design and implementation on FPGA, Lecture Notes in Computer Science Vol.8537: Lecture Notes in Artificial Intelligence, Rough sets and intelligent systems paradigms : Second International Conference : RSEISP2014 : Held as Part of JRS 2014, eds. Marzena Kryszkiewicz, Chris Cornelis, Dave Ciucci, Jesus Medina-Moreno, Hiroshi Motoda, Zbigniew W. Raś, Berlin, Springer-Verlag, 2014, pp. 209-216. doi: 10.1007/978-3-319-08729-0_20.
• [8] Kopczyński M, Grześ T, Stepaniuk J. Core for Large Datasets : Rough Sets on FPGA, Concurrency, Specification & Programming : 24th International Workshop : CS&P 2015 Vol.1, eds. Zbigniew Suraj, Ludwik Czaja, Rzeszow, University of Rzeszow, 2015, pp. 235-246.
• [9] Lewis T, Perkowski M, Jozwiak L. Learning in Hardware: Architecture and Implementation of an FPGA-Based Rough Set Machine, Euromicro, vol. 1, 25th Euromicro Conference (EUROMICRO ’99)-Volume 1, 1999, pp. 1326. doi: 10.1109/EURMIC.1999.794488.
• [10] Lichman M. UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science (2013). Available from: http://archive.ics.uci.edu/ml.
• [11] Mańkowski M, Łuba T, Jankowski C. Evaluation of Decision Table Decomposition Using Dynamic Programming Classifiers, Concurrency, Specification & Programming : 24th International Workshop : CS&P 2015 Vol.2, eds. Zbigniew Suraj, Ludwik Czaja, Rzeszow, University of Rzeszow, 2015, pp. 34-43. Available from: http://ceur-ws.org/Vol-1492/Paper_28.pdf.
• [12] Muraszkiewicz M, Rybiński H. Towards a Parallel Rough Sets Computer In: Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag, 1994, pp. 434–443. doi: 10.1007/978-1-4471-3238-7_51.
• [13] Pawlak Z. Elementary rough set granules: Toward a rough set processor. In: S. K. Pal, L. Polkowski, and A. Skowron, editors, Rough-Neurocomputing: Techniques for Computing with Words, Cognitive Technologies. Springer-Verlag, Berlin, Germany, 2004, pp. 5–14.
• [14] Stepaniuk J. Knowledge discovery by application of rough set models. In: L. Polkowski, S. Tsumoto, T.Y. Lin (Eds.), Rough Set Methods and Applications. New Developments in Knowledge Discovery in Information Systems, Physica–Verlag, Heidelberg, 2000, 137–233. doi: 10.1007/978-3-7908-1840-6_5.
• [15] Stepaniuk J. Rough–Granular Computing in Knowledge Discovery and Data Mining, Springer Publishing Company, Incorporated, 2008. ISBN: 3540708006, 9783540708001.
• [16] Stepaniuk J, Kopczyński M, Grześ T. The First Step Toward Processor for Rough Set Methods, Fundamenta Informaticae Vol. 127, 2013, pp. 429-443. doi: 10.3233/FI-2013-919.
• [17] Tiwari KS, Kothari AG. Design and Implementation of Rough Set Algorithms on FPGA: A Survey, in: (IJARAI) International Journal of Advanced Research in Artificial Intelligence, 2014;3(9):14–23. doi: 10.14569/IJARAI.2014.030903.
• [18] Witten I, Frank E, Hall MA. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011. ISBN: 978-0-12-374856-0.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).