基于属性递减策略的属性约简递归算法

属性约简是粗糙集理论的核心研究内容之一。通过对某些现存属性约简算法分析，发现它们并不能有效地或正确地获取约简结果。为此，论文提出了一种基于属性递减策略的属性约简递归算法，该算法首先求出每个条件属性的依赖度，然后依次从条件属性集中减去依赖度较小的属性，并判断剩余属性集依赖度是否为1，如果是，则算法递归执行。最后把所获属性集并入约简集并求得核。该算法不仅能够快速计算出所有约简和核，而且运算简单、计算量较少，从而提高了算法效率。实例验证表明，该算法能更有效地对决策表进行约简，具有很强的实用性。

Attribute Reduction Recursion Algorithm Based on Attribute Diminishing Strategy

Attribute reduction is one of core research subjects in rough set theory. By means of analyzing some existing attribute reduction algorithms, it found that they cannot effectively or correctly get reduction results. An attribute reduction recursion algorithm based on attribute diminishing strategy was presented in this paper. The proposed attribute reduction algorithm firstly calculate dependency degree of every condition attribute; and then in turn subtract condition attributes with smaller dependency degree; subsequently, calculate dependency degree of remaining attributes set and decide whether the dependency degree is 1, if it is, the algorithm is recursively implemented; Finally, the all won attributes set were merged into the attribute reduction set and the core attributes were obtained. The proposed attribute reduction algorithm can not only fast calculate out all attribute reduction and core attributes, but also operates simply and has less computation, so that efficiency of the algorithm is improved. The experiment shows that the proposed attribute reduction algorithm can more effectively reduct decision table and have stronger practicability.