用于特征选择的乌鸦搜索算法的研究与改进

Research and Improvements on Crow Search Algorithm for Feature Selection

特征选择是机器学习领域的热点问题.元启发式算法作为特征选择的重要方法之一,其性能会对问题求解产生直接影响.乌鸦搜索算法(CSA)是受乌鸦智能群体行为启发提出的一种元启发式算法,由于其具有简单、高效的特点,广大学者将其用来解决特征选择问题.然而,CSA易陷入局部最优解且收敛速度较慢,严重限制了算法求解能力.针对这一问题,采用logistic混沌映射、反向学习方法和差分进化这3种算子,结合乌鸦搜索算法,提出一种特征选择算法BICSA来选取最优特征子集.实验阶段,使用UCI数据库中的16个数据集来测试BICSA的性能.实验结果表明,与其他特征选择算法相比,BICSA求得的特征子集具有更高的分类准确率和较高的维度压缩能力,这说明BICSA在处理特征选择问题上具有很强的竞争力与足够的优越性.

Feature selection is a hot issue in the field of machine learning.Meta-heuristic algorithm is one of the important methods of feature selection,and its performance will have a direct impact on problem solving.Crow search algorithm(CSA)is a kind of meta-heuristic algorithm inspired by the behavior of crow intelligent group.Because of its simple and efficient characteristics,it is used by many scholars to solve the feature selection problem.However,CSA is easy to fall into a local optimal solution and the convergence speed is slow,which severely limits the algorithm’s solving ability.In response to this problem,this study uses three operators,namely,logistic chaotic mapping,opposition-based learning method,and differential evolution,combined with crow search algorithm,proposes a feature selection algorithm BICSA to select the optimal feature subset.In the experimental phase,the performance of BICSA was demonstrated by using 16 data sets in the UCI database.Experimental results show that compared with other feature selection algorithms,the feature subset obtained by BICSA has higher classification accuracy and higher dimensional compression capabilities,indicating that BICSA has the ability to deal with feature selection problems with strong competitiveness and sufficient superiority.