基于粗糙集的多分类器集成学习算法

Multiple classifiers ensemble learning algorithm based on rough set theory

为将粗糙集理论与集成学习进行有效的结合,提高多分类器集成的分类性能,提出一种结合粗糙集的多分类器集成学习算法。根据粗糙集理论,将样本划分为正区域和边界域两部分,在此基础上进行样本抽样;抽样过程中,确保抽样的每个数据集都包括边界域内的所有样本。在UCI数据集上的实验结果表明,相比一些经典的集成算法,该算法在Precision、Recall等多个指标上提高了对数据分类的准确度。

To combine the rough sets theory and ensemble learning more effectively and improve the classification performance of multiple classifier ensembles,a multiple classifier ensemble learning algorithm combining rough set with ensemble learning was proposed.Based on rough set theory,samples were divided into positive region and boundary region,which was useful for training data sampling.During the sampling process,all samples within the boundaries were included in each sampling data set.Experimental results on UCI data set indicate that the proposed algorithm can get better performance as for precision and recall compared to traditional methods.