Title : 
Application of variable precision rough set attribute reduction algorithm
         
        
            Author : 
Cai-Yun, Zhang ; Jing, Wang ; Hui, Wang
         
        
            Author_Institution : 
Nat. Eng. Res. Center of Adv. Rolling, Univ. of Sci. & Technol. Beijing, Beijing, China
         
        
        
        
        
            Abstract : 
In order to remove the drawbacks of the classical attribute reduction algorithm based on attribute importance which can´t ensure to gain the simplest decision table and the best noise resisting ability, this paper presents a improved attribute reduction algorithm based on β - variable precision rough sets. A parameter β represents the error resolution and makes the decision table more simple and reliable. Compared with the classical attribution reduction algorithm, β - variable precision rough sets attribute reduction algorithm has better generalization and ability of resisting noise. These two algorithms are used on the simulation of Car Test Results and the results verify the superiority of the improved algorithm.
         
        
            Keywords : 
decision tables; rough set theory; β-variable precision rough sets; car test results; decision table; error resolution; noise resisting ability; rough attribute set reduction algorithm; attribute reduction; rough sets; variable precision;
         
        
        
        
            Conference_Titel : 
Computer Application and System Modeling (ICCASM), 2010 International Conference on
         
        
            Conference_Location : 
Taiyuan
         
        
            Print_ISBN : 
978-1-4244-7235-2
         
        
            Electronic_ISBN : 
978-1-4244-7237-6
         
        
        
            DOI : 
10.1109/ICCASM.2010.5620438