论文信息 - Attribute significance for F — Parallel reducts

Attribute significance for F — Parallel reducts

Attribute significance in a family of decision subsystems is defined in this paper, and its properties are discussed. It is the extension of attribute significance for a single decision system. We apply it to obtain parallel reducts, and an algorithm with the attribute significance in a family of decision subsystems is proposed. Experimental results show that the method overmatches the matrix of attribute significance in both time complexity and space complexity as well as the length of reducts. Moreover, a new rough set model called F-rough sets is proposed, it is consistent with parallel reducts.

[1] Liu Shao. A New Method for Fast Computing Positive Region , 2003 .

[2] Xiangjun Li,et al. (F, epsilon)-Parallel Reducts in a Series of Decision Subsystems , 2009, 2010 Third International Joint Conference on Computational Science and Optimization.

[3] Wang Guo,et al. Decision Table Reduction based on Conditional Information Entropy , 2002 .

[4] Dayong Deng. (F, epsilon)-Parallel Reducts in a Series of Decision Subsystems , 2010, CSO 2010.

[5] XIAOHUA Hu,et al. LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH , 1995, Comput. Intell..

[6] Dayong Deng,et al. Parallel reduct and its properties , 2009, 2009 IEEE International Conference on Granular Computing.

[7] Houkuan Huang,et al. A New Discernibility Matrix and Function , 2006, RSKT.

[8] Andrzej Skowron,et al. The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[9] Miao Duo,et al. An Information Representation of the Concepts and Operations in Rough Set Theory , 1999 .

[10] Jan G. Bazan,et al. Rough set algorithms in classification problem , 2000 .

[11] Dayong Deng,et al. Parallel Reducts Based on Attribute Significance , 2010, RSKT.

[12] Janusz Zalewski,et al. Rough sets: Theoretical aspects of reasoning about data , 1996 .