重要度集成的属性约简方法研究

Research on ensemble significance based attribute reduction approach

启发式算法在求解约简的过程中逐步加入重要度最高的属性,但其忽视了数据扰动将会直接引起重要度计算的波动问题,从而造成约简结果的不稳定。鉴于此,提出了一种基于集成属性重要度的启发式算法框架。首先,在原始数据上进行多重采样;然后,在每次循环过程中分别计算各个采样结果上的属性重要度并对这些重要度进行集成;最后,将集成重要度最大的属性加入到约简中去。利用邻域粗糙集方法进行的实验结果表明,基于集成重要度的属性约简算法不仅能够获取更加稳定的约简,而且利用所生成的约简能够得到一致性较高的分类结果。

In the process of computing reduct using a heuristic algorithm,the attribute with the highest importance is gradually added in.However,this approach neglects the fluctuation of important calculations which is directly caused by data perturbation.Notably,such fluctuation may lead to an unstable reduct result.To eliminate such an anomaly,a framework consisting of a heuristic algorithm based on the importance of the ensemble attribute was proposed.In this approach,firstly,multiple sampling is executed for raw data;secondly,in each cycle,the importance of each attribute is computed on the basis of each sampling and the importance indices are integrated;finally,the attribute with the highest importance is added into the reduct.The experimental results obtained by utilizing the neighborhood rough set method show that the new approach not only obtains a more stable reduct,but also attains the classification results with high uniformity.