Attribute reduction algorithms of fuzzy rules based on?continuous domain condition attributes
CUI Meng-tian?1,ZHU Hao-dong?2,ZHONG Yong?2?(1.School of Computer Science & Technology, Southwest University for Nationalities, Chengdu 610041, China;2.Chengdu Institute of ?Computer Applications, Chinese Academy of Sciences, Chengdu610041, China)
Abstract:To solve the problems of low adaptability for continuous domain reduction and the disadvantage of failing to obtain eventual relationship among the fuzzy sets,this paper proposed a new method of attribute reduction algorithms of decision table based on combining fuzzy set with rough set. First,transformed continuous attribute value into fuzzy value with triangular membership function,then provided algorithms of hard C-means(HCM) clustering to obtain relationship among the fuzzy sets.In the end,simulation results show the effectiveness of the proposed method through an illustrative example.
Key words:condition attributes; continuous; membership function; fuzzy rules
[t(R)]=10.560.56?0.5610.56?0.560.561
取λ=0.8可得
[t(R)]?λ=1 0 0?0 1 0?0 0 1
在模糊等价矩阵的截集阈值λ=0.8的条件下,各连续条件属性是不相关的。因此表1的主观约简集为{c?1,c?2,c?3},这个结果与文献[8]所得的结果完全一致。
通过这个实例说明,利用本文算法不仅能够解决连续域决策表属性约简问题,而且还可以根据需要获得主观的属性约简集和一组模糊规则集,这说明本算法是可行的。
5 结束语
本文针对粗糙集对于连续域属性决策表的处理能力差以及不容易获得模糊集之间关系等问题,提出一种把模糊集和粗糙集结合起来的连续型条件属性模糊规则约简算法。实例验证表明,采用该算法,用户可以根据实际决策需要和领域知识更改阈值,从而获得满意的模糊规则结果。
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