基于邻域粗糙集和蚁群优化的属性约简算法

Approach to feature selection based on neighborhood rough set and ant colony optimization

在分析单一、给定的邻域大小设定方法弊端的基础上,提出了基于属性数据标准差的阈值设定方法,并将蚁群优化算法引入到属性约简中,以属性重要度为启发信息,构造了基于邻域粗糙集和蚁群优化的属性约简算法,使用了4个UCI数据集进行约简。实验结果表明,提出的算法在约简的分类精度和约简中属性个数方面具有更好的性能。

This paper analyses the weakness of setting a single,specified threshold for the size of neighborhood,and then puts forward a new neighborhood setting method based on the standard deviation of feature data.The paper introduces ant colong opbimization（ACO） into feature selection and proposes an approved feature selection algorithm based on NRS and ACO,in which the feature importance is taken as the heuristic information.In order to evaluate the performance of the proposed algorithm,four datasets from UCI are used and the experimental results show that the proposed algorithm has a better performance in classification accuracy of reduct and feature number in reduct.