Abstract
A hashing approach in parallel reducts is clearly presented in this paper. With the help of this new approach, time-consuming comparison operations reduce significantly, therefore, matrix of attribute significance can be calculated more efficiently. Experiments show that our method has advantage over PRMAS, our classical parallel reducts method.
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References
Pawlak, Z.: Rough Sets-Theoretical Aspect of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Liu, Q.: Rough Sets and Rough Reasoning. Science Press (2001) (in Chinese)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1991)
Hu, X., Cercone, N.: Learning in Relational Databases: A Rough Set Approach. Computational Intelligence 11(2), 323–337 (1995)
Deng, D., Huang, H.: A New Discernibility Matrix and Function. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 114–121. Springer, Heidelberg (2006)
Miao, D., Wang, J.: An Information Representation of the Concepts and Operations in Rough Set Theory. Chinese Journal of Software 10(2), 113–116 (1999)
Wang, G., Yu, H., Yang, D.: Decision Table Reduction based on Conditional Information Entropy. Chinese Journal of Computers 25(7), 759–766 (2002)
Bazan, G.J.: A Comparison of Dynamic Non-dynamic Rough Set Methods for Extracting Laws from Decision Tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 321–365. Physica-Verlag, Heidelberg (1998)
Bazan, G.J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J.: Rough Set Algorithms in Classification Problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag (2000)
Deng, D., Wang, J., Li, X.: Parallel Reducts in a Series of Decision Subsystems. In: Proceedings of the Second International Joint Conference on Computational Sciences and Optimization (CSO 2009), Sanya, Hainan, China, pp. 377–380 (2009)
Deng, D.: Comparison of Parallel Reducts and Dynamic Reducts in Theory. Computer Science 36(8A), 176–178 (2009) (in Chinese)
Deng, D.: Parallel Reducts and Its Properties. In: Proceedings of 2009 IEEE International Conference on Granular Computing, pp. 121–125 (2009)
Deng, D.: (F, ε)-Parallel Reducts in a Series of Decision Subsystems. In: Proceedings of the Third International Joint Conference on Computational Sciences and Optimization(CSO 2010), pp. 372–376 (2010)
Deng, D., Yan, D., Wang, J.: Parallel Reducts Based on Attribute Significance. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS (LNAI), vol. 6401, pp. 336–343. Springer, Heidelberg (2010)
Deng, D., Yan, D., Chen, L.: Attribute Significance for F-Parallel Reducts. In: Proceedings of 2011 IEEE International Conference on Granular Computing, pp. 156–161 (2011)
Knuth, D.E.: Sorting and Searching, 2nd edn. The Art of Computer Programming, vol. 3. Addison-Wesley (1998)
Wang, P.C.: Efficient hash-based approximate reduct generation. In: Proceedings of 2011 IEEE International Conference on Granular Computing, pp. 703–707 (2011)
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Pei, M., Deng, D., Huang, H. (2013). Parallel Reducts: A Hashing Approach. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_22
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DOI: https://doi.org/10.1007/978-3-642-41299-8_22
Publisher Name: Springer, Berlin, Heidelberg
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