摘要
对传统的粗糙集理论进行了扩展,提出了一种改进的粗糙集归纳学习方法。一方面,针对连续属性离散化,利用模糊集理论对连续属性进行模糊化,再根据模糊贴近度构造模糊相似矩阵,并用k-w方法粗略评估各连续属性的重要度,建立基于模糊相似关系的划分,最终生成相容的决策表。另一方面,针对解决最优属性的选择问题,提出一种加权求和的属性重要度定义。基于以上模型开发了一个原型系统,并以一个工程实例验证了此方法的有效性。
In this paper,an inductive learning approach based on modified rough set is proposed. Firstly, the continuous attributes in the decision table are fuzzified with the proper fuzzy membership functions,and the fuzzy similar matrix of the attributes is constructed with the fuzzy degree of nearness, then the k-w method is applied to evaluate the relative importance of every continuous attribute. The continuous decision table is discretized into a compatible table based on the fuzzy similarity relation. Secondly, an improved definition of the attribute significance based on the weighed sum is proposed, A prototype system based on the proposed approach is developed, Finally, an engineering example proves the effectiveness and feasibility of the propused method.
出处
《计算机技术与发展》
2006年第10期32-34,36,共4页
Computer Technology and Development
基金
国家"八六三"计划资助项目(2002AA134020)
上海市高校青年教师科研专项基金(05XPYQ45)
关键词
离散化
粗糙集
模糊相似关系
属性重要度
归纳学习
discretization
rough set
fuzzy similarity relation
attribute significance
inductive learning
