一种集成遗传算法与模糊推理的粗糙集数据分析算法

A Rough Sets Data Analysis Algorithm Combined with Genetic Algorithms and Fuzzy Inference

粗糙集数据分析的主要优点在于它不要求任何关于被处理数据的先验或额外的知识,文章利用其对数据库进行分析计算,自动获取数据库在各个层次上的规则集。在保证量化后的数据库具有最大一致性的前提下,利用遗传算法求取连续属性值的最优量化区间个数及各个区间分点值。同时将量化区间进行模糊化,将清晰规则集转化为模糊规则集,利用模糊推理进行决策以提高鲁棒性。通过对UCI中几个数据库的测试验证了所提出算法的有效性。

The main advantage of rough sets data analysis is that it doesn't require any prior or additional knowledge about the data,which is then used in this paper to analysis the database,acquiring automatically the hierarchical rule sets.In order to ensure maximum consistency of the quantification data,the genetic algorithms is used to get the optimal number and points of division of quantification intervals.At the same time the quantification intervals is fuzzified and crisp rule sets are then transformed to fuzzy rule sets.Then the fuzzy inference is conducted to enhance the robustness.The validity of the proposed algorithm is proved through the test on some databases of the UCI repository.