Rule induction from inconsistent and incomplete data using rough sets
Rule induction from inconsistent and incomplete data using rough sets
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DOI：
10.1109/icsmc.1999.815540
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发表时间：
1999-10
期刊：
影响因子：
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通讯作者：
R. Félix;T. Ushio
中科院分区：
文献类型：
--
作者：
R. Félix;T. Ushio
提出了两种基于粗糙集理论的获取不一致和不完备信息系统最小规则的方法。这两种方法都利用二进制可辨矩阵的定义，在搜索最小覆盖时用逐位运算代替集合运算。第一种方法是对覆盖物进行穷举搜索，第二种方法使用基于遗传算法的搜索。利用上下近似解决了不一致问题，并将实例对之间的可辨性定义修改为粗糙可辨性（即肯定可辨性和可能不可辨性）来解决不完备性问题。
Proposes two methods based on rough sets theory to obtain minimal rules in an information system with inconsistencies and incompleteness. Both methods make use of the definition of a binary discernibility matrix to replace sets operations by bit-wise operations in the search of minimal coverings. The first method is an exhaustive search of coverings and the second uses a genetic algorithm (GA) based search. Inconsistencies are solved with the lower and upper approximations and the incompleteness problem is faced by modifying the definition of discernibility between pairs of examples into a rough discernibility (i.e. surely discernible and possibly indiscernible).