基于代价敏感和近似分类质量的 决策粗糙集属性约简研究.

Aiming at the low precision problem while the cost was introduced into attribute reduction of decision-theoretic rough set, this paper studied the balance between the total cost and the precision in classification. It used the total cost of the classification and the approximate classification quality as the constrained criteria in the attribute reduction procedure, combined with simulated annealing method, and proposed a DTRS attribute reduction algorithm constrained by cost-sensitive and classification quality (hereinafter referred as ARACOQ). It carried out the simulation experiments by using UC1 data set. The results verify the effectiveness of the ARACOQ algorithm. which can find an attribute reduction set with the highest classification precision within the affordable cost range. [ABSTRACT FROM AUTHOR]