The Attribute Reduce Based on Rough Sets and SAT Algorithm
Jianguo Wang, Guoyan Meng, Xiaolong Zheng · 2008
Rough set theory introduced by Z..Pawlak in the early 1980s is a mathematical tool of reasoning about data. In recent years it has received much attention of the researchers around the world. Rough set theory has been successfully applied to many areas including machine learning, pattern recognition, decision analysis, process control, knowledge discovery from databases. An algorithm in finding minimal reduction based on Prepositional Satisfiability (abbreviated as SAT) algorithm is proposed. A branch and bound algorithm is presented to solve the proposed SAT problem. The experimental result shows that the proposed algorithm has significantly reduced the number of rules generated form the obtained reduction with high percentage of classification accuracy.