基于粗糙集的属性约简优化算法研究

Research of optimization algorithm of attribute reduction based on rough sets

属性约简是粗糙集理论中一个核心研究问题,在对粗糙集中属性约简相关理论研究的基础上,提出了一种新的基于属性重要性和依赖性相结合的GENRED_GROWTH属性约简算法.并通过CUI机器学习数据集测试实验,验证了该算法的有效性.

Attribute reduction is one of the key problems for the rough sets theory. After the basic theories of the Rough Sets are studies,a new algorithm called GENRED_GROWTH that combining the attributes of dependence and importance is presented. The test results by UCI Machine Learning Date Sets demonstrate that the algorithm is effective.