By Topic

Decision table decomposition using core attributes partition for attribute reduction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Mingquan Ye ; Computer Staff Room, WanNan Medical College, WuHu 241002, China ; Changrong Wu

The attribute reduction algorithms of decision table based on discernability matrix are required to construct discernability matrix, which reduces efficiency of algorithms. In this paper, a decision table decomposition model is proposed to solve the attribute reduction problem based on discernibility matrix for large decision table. By introducing the core attributes partition, the large decision table is divided into a number of decision sub-tables, which translates computing discernibility matrix in original decision table into computing discernibility matrix in decision sub-tables. The relationships between all the minimum attribute reductions of original decision table and all the attribution reductions of its decision sub-tables are first established. Based on the idea, a complete algorithm is presented, and all the minimum attribute reductions in the original decision table can be obtained from attribute reduction in its decision sub-tables. Theoretical analysis and numerical example results indicate that the algorithm can more easily explore all the minimum attribute reductions, and it is efficient.

Published in:

Computer Science and Education (ICCSE), 2010 5th International Conference on

Date of Conference:

24-27 Aug. 2010