一种基于粗糙—模糊集理论的知识获取方法

该文提出了一种基于粗糙-模糊集理论的知识获取方法,该方法将粗糙集理论与模糊集理论相结合,先利用模糊集理论对决策表的连续属性进行模糊化,通过构建模糊相似矩阵进而划分论域;再利用粗糙模糊集理论进行属性约简,从而获取决策规则。最后,通过实例验证了该方法的有效性和实用性。

Knowledge Acquisition Approach Based on Rough-Fuzzy Sets

This article presents an approach of knowledge acquisition based on rough and fuzzy sets, which combines features of rough sets and fuzzy sets. The continuous attributes in the decision table are fuzzified with fuzzy membership functions. The domain partition is accomplished after establishing fuzzy similarity matrix. Attributes reduction can be obtained using rough-fuzzy sets, and then decision rules can be acquired. At last, an example is illustrated and proves the approach is effective and practical.