基于改进斑点鬣狗优化算法的同步优化特征选择

Simultaneous feature selection optimization based on improved spotted hyena optimizer algorithm

针对传统支持向量机(SVM)在封装式特征选择中分类精度低、特征子集选择冗余以及计算效率差的不足,利用元启发式优化算法同步优化SVM与特征选择。为改善SVM分类效果以及选择特征子集的能力,首先,利用自适应差分进化(DE)算法、混沌初始化与锦标赛选择策略对斑点鬣狗优化(SHO)算法改进,以增强其局部搜索能力并提高其寻优效率与求解精度;其次,将改进后的算法用于特征选择与SVM参数调整的同步优化中;最后,在UCI数据集进行特征选择仿真实验,采取分类准确率、选择特征数、适应度值及运行时间来综合评估所提算法的优化性能。实验结果证明,改进算法的同步优化机制能够在高分类准确率下降低特征选择的数目,该算法比传统算法更适合解决封装式特征选择问题,具有良好的应用价值。

Aiming at the disadvantages of traditional Support Vector Machine(SVM)in the wrapper feature selection:low classification accuracy,redundant feature subset selection and poor computational efficiency,the meta-heuristic optimization algorithm was used to simultaneously optimize SVM and feature selection.In order to improve the classification effect of SVM and the ability of feature subset selection,firstly,the Spotted Hyena Optimizer(SHO)algorithm was improved by using the adaptive Differential Evolution(DE)algorithm,chaotic initialization and tournament selection strategy,so as to enhance its local search ability as well as improve its optimization efficiency and solution accuracy;secondly,the improved algorithm was applied to the simultaneous optimization of feature selection and SVM parameter adjustment;finally,a feature selection simulation experiment was carried out on the UCI datasets,and the classification accuracy,the number of selected features,the fitness value and the running time were used to comprehensively evaluate the optimization performance of the proposed algorithm.Experimental results show that the simultaneous optimization mechanism of the improved algorithm can reduce the number of selected features with high classification accuracy,and compared to the traditional algorithms,this algorithm is more suitable for solving the problem of wrapper feature selection,which has good application value.