计算机应用研究2011，Vol.28Issue(7)：2436-2438,3.DOI:10.3969/j.issn.1001-3695.2011.07.008
基于粗糙集与蚁群优化算法的特征选择方法研究
Rough sets and ant colony optimization based feature selection
摘要
Abstract
Many existing ACO-based feature selection algorithms start from a random dot,which aim at finding the optimal fea-tures. This thesis analyzed the feature core method of rough sets and the global optimization ability of ACO, proposed a new rough set approach to feature selection based on ACO,which adopted feature significance as heuristic information. The approach started from the feature core,which changed the complete graph to a smaller one. To verify the efficiency of algorithm,carried out experiments on some standard UC1 datasets. The results demonstrate that the proposed algorithm can provide efficient solu-tion to find a minimal subset of the features.关键词
粗糙集理论/知识约简/特征选择/蚁群优化Key words
rough set theory/ knowledge reduction/ feature selection/ ant colony optimization分类
计算机与自动化引用本文复制引用
吴克寿,陈玉明,谢荣生,王晓栋..基于粗糙集与蚁群优化算法的特征选择方法研究[J].计算机应用研究,2011,28(7):2436-2438,3.基金项目
国家自然科学基金资助项目(60903203) （60903203）
