摘要
为了提高特征选择方法的计算速度,提出基于Levy flight随机过程的特征选择方法.该方法在寻优过程中定义基于启发式的分阶段搜索策略,在局部搜索行为中引入Levy flight随机过程,将Levy flight距离与搜索行为进行映射.在不同的搜索阶段,利用不同的映射区间改变搜索行为出现的概率,以该映射来控制局部搜索行为的方向和速度,从而避免了陷入局部最优的问题.实验结果表明,采用LevyFS算法克服了启发式特征选择方法的局限性,平均耗时仅为SFFS算法的1/3左右.
A Levy flight random process based feature selection algorithm（LevyFS） was proposed in order to improve the speed of feature selection method.A multi-stages heuristic search strategy was defined during optimization process.Levy flight random process was introduced in local search behavior,and map between Levy flight distances and search operations was defined.During different search stages,map was used to change the probability of search behavior so as to control the direction and speed of local search behavior.Then local optimum was prevented.Experimental results show that LevyFS algorithm overcomes the limitation of heuristic methods and the average time cost of LevyFS algorithm is only one-third time cost of SFFS algorithm.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2013年第4期638-643,共6页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(60772092
81101903)
