Features Reduction Using Logic Minimization Techniques

• Autorzy: Borowik G. , Łuba T. , Zydek D.
• Konferencja: International Conference on System Engineering - ICSEng (21 ; 16-18.08.2011) ; Las Vegas, USA
• Języki publikacji: EN

Abstrakty

EN

This paper is dedicated to two seemingly different problems. The first one concerns information theory and the second one is connected to logic synthesis methods. The reason why these issues are considered together is the important task of the efficient representation of data in information systems and as well as in logic systems. An efficient algorithm to solve the task of attributes/arguments reduction is presented.

Słowa kluczowe

EN

machine learning; knowledge representation; discernibility function; logic minimization; attribute reduction; complement

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2012
• Tom: Vol, 58, No. 1
• Strony: 71--76
• Opis fizyczny: Bibliogr. 26 poz.

Twórcy

autor

Borowik G.

• Institute of Telecommunications, Warsaw University of Technology, Warsaw, Poland

autor

Łuba T.

• Institute of Telecommunications, Warsaw University of Technology, Warsaw, Poland

autor

Zydek D.

• Department of Electrical Engineering, Idaho State University, USA

Bibliografia

• [1] S. Abdullah, L. Golafshan, and Mohd Zakree Ahmad Nazri, “Reheat simulated annealing algorithm for rough set attribute reduction,” International Journal of the Physical Sciences, vol. 6, no. 8, pp. 2083–2089, 2011.
• [2] J. Bazan, H. S. Nguyen, S. H. Nguyen, P. Synak, and J. Wróblewski, “Rough set algorithms in classification problem,” in Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Heidelberg: Physica-Verlag, 2000, vol. 56, pp. 49–88.
• [3] R. Dash, R. Dash, and D. Mishra, “A hybridized rough-PCA approach of attribute reduction for high dimensional data set,” European Journal of Scientific Research, vol. 44, no. 1, pp. 29–38, 2010.
• [4] Z. Feixiang, Z. Yingjun, and Z. Li, “An efficient attribute reduction in decision information systems,” in International Conference on Computer Science and Software Engineering, Wuhan, Hubei, 2008, pp. 466–469, DOI: 10.1109/CSSE.2008.1090.
• [5] A.-R. Hedar, J. Wang, and M. Fukushima, “Tabu search for attribute reduction in rough set theory,” Journal of Soft Computing – A Fusion of Foundations, Methodologies and Applications, vol. 12, no. 9, pp. 909–918, Apr. 2008, DOI: 10.1007/s00500-007-0260-1.
• [6] S. Jing and K. She, “Heterogeneous attribute reduction in noisy system based on a generalized neighborhood rough sets model,” World Academy of Science, Engineering and Technology, vol. 75, pp. 1067–1072, 2011.
• [7] P. Kalyani and M. Karnan, “A new implementation of attribute reduction using quick relative reduct algorithm,” International Journal of Internet Computing, vol. 1, no. 1, pp. 99–102, 2011.
• [8] M. Kryszkiewicz and K. Cichoń, “Towards scalable algorithms for discovering rough set reducts,” in Transactions on Rough Sets I, ser. Lecture Notes in Computer Science, J. Peters, A. Skowron, J. Grzymała-Busse, B. Kostek, R. Świniarski, and M. Szczuka, Eds. Berlin: Springer Berlin / Heidelberg, 2004, vol. 3100, pp. 120–143, DOI: 10.1007/978-3-540-27794-1 5.
• [9] M. Kryszkiewicz and P. Lasek, “FUN: Fast discovery of minimal sets of attributes functionally determining a decision attribute,” in Transactions on Rough Sets IX, ser. Lecture Notes in Computer Science, J. Peters, A. Skowron, and H. Rybiński, Eds. Springer Berlin / Heidelberg, 2008, vol. 5390, pp. 76–95, DOI: 10.1007/978-3-540-89876-4 5.
• [10] D. Nguyen and X. Nguyen, “A new method to attribute reduction of decision systems with covering rough sets,” Georgian Electronic Scientific Journal: Computer Science and Telecommunications, vol. 1, no. 24, pp. 24–31, 2010.
• [11] Z. Pawlak, Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, 1991.
• [12] X. Pei and Y. Wang, “An approximate approach to attribute reduction,” International Journal of Information Technology, vol. 12, no. 4, pp. 128–135, 2006.
• [13] A. Skowron and C. Rauszer, “The discernibility matrices and functions in information systems,” in Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory, R. Słowiński, Ed.Kluwer Academic Publishers, 1992.
• [14] C. Wang and F. Ou, “An attribute reduction algorithm based on conditional entropy and frequency of attributes,” in Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation, ser. ICICTA ’08, vol. 1. Washington, DC, USA: IEEE Computer Society, 2008, pp. 752–756, DOI: 10.1109/ICICTA.2008.95.
• [15] Y. Yao and Y. Zhao, “Attribute reduction in decision-theoretic rough set models,” Information Sciences, vol. 178, no. 17, pp. 3356–3373, 2008, DOI: 10.1016/j.ins.2008.05.010.
• [16] T. Łuba, R. Lasocki, and J. Rybnik, “An implementation of decomposition algorithm and its application in information systems analysis and logic synthesis,” in Rough Sets, Fuzzy Sets and Knowledge Discovery, W. Ziarko, Ed. Springer Verlag, 1994, pp. 458–465, Workshops in Computing Series.
• [17] T. Łuba and R. Lasocki, “On unknown attribute values in functional dependencies,” in Proceedings of The Third International Workshop on Rough Sets and Soft Computing, San Jose, 1994, pp. 490–497.
• [18] M. Rawski, G. Borowik, T. Łuba, P. Tomaszewicz, and B. J. Falkowski, “Logic synthesis strategy for FPGAs with embedded memory blocks,” Electrical Review, vol. 86, no. 11a, pp. 94–101, 2010.
• [19] H. Selvaraj, P. Sapiecha, M. Rawski, and T. Łuba, “Functional decomposition – the value and implication for both neural networks and digital designing,” International Journal of Computational Intelligence and Applications, vol. 6, no. 1, pp. 123–138, March 2006, DOI: 10.1142/S1469026806001782.
• [20] G. Borowik, T. Łuba, and D. Zydek, “Reduction of knowledge representation using logic minimization techniques,” in ICSEng, 2011, pp. 482–485, DOI: 10.1109/ICSEng.2011.98.
• [21] J. A. Brzozowski and T. Łuba, “Decomposition of boolean functions specified by cubes,” Journal of Multi-Valued Logic & Soft Computing, vol. 9, pp. 377–417, 2003.
• [22] T. Łuba and J. Rybnik, “Rough sets and some aspects in logic synthesis,” in Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory, R. Słowiński, Ed. Kluwer Academic Publishers, 1992.
• [23] R. K. Brayton, G. D. Hachtel, C. T. McMullen, and A. Sangiovanni-Vincentelli, Logic Minimization Algorithms for VLSI Synthesis. Kluwer Academic Publishers, 1984.
• [24] “ROSETTA – A Rough Set Toolkit for Analysis of Data.” [Online]. Available: http://www.lcb.uu.se/tools/rosetta/
• [25] “RSES – Rough Set Exploration System.” [Online]. Available: http://logic.mimuw.edu.pl/rses/
• [26] “ROSE2 – Rough Sets Data Explorer.” [Online]. Available: http://idss.cs.put.poznan.pl/site/rose.html