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

作　　者：吴克寿[1] 陈玉明[1] 谢荣生[1] 王晓栋[1] 

机构地区：[1]厦门理工学院计算机科学与技术系,福建厦门361024

出　　处：《计算机应用研究》2011年第7期2436-2438,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(60903203)

摘　　要：已有的基于蚁群优化算法的特征选择方法是从随机点出发,寻找最优的特征组合。讨论和分析了粗糙集理论中的特征核思想,结合蚁群优化算法的全局寻优特点,以特征重要度作为启发式搜索信息,提出从特征核出发基于粗糙集理论与蚁群优化的特征选择算法,简化蚁群完全图搜索的规模。在标准UCI数据集上进行测试,实验验证了新算法对于特征选择的有效性。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 UCI datasets. The results demonstrate that the proposed algorithm can provide efficient solution 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]