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针对高维数据集结构复杂且冗余度高的问题，提出一种新型二进制人工蜂群算法进行特征选择。该算法在雇佣蜂蜜源搜索阶段应用差分思想，增加多项式差分变异算子，实现蜜源更新环节的多维性、高效性；在跟随蜂阶段和侦察蜂阶段分别引入交叉算子和最优保存策略，进一步打破局部最优，有效提升了人工蜂群算法的收敛效果；对蜜源的二进制初始化处理，使得算法在特征选择过程中取得了良好表现。在4个Benchmark测试函数上进行实验，结果表明，新算法的寻优精度和收敛速度优于其他4种经典搜索算法。同时，选取7个常用高维数据集进行特征选择，并与7种经典降维算法进行对比，发现新算法的特征约简程度普遍高于88%,并且随着数据集维度的增高，新算法的降维程度和分类精度优于其他7种降维算法。
Abstract：Aiming at the problems of complex structure and high redundancy of high-dimensional data sets, a new binary artificial bee colony algorithm was proposed for feature selection.In the algorithm, difference idea was applied and polynomial difference mutation operator was added to the stage of hired honey source search, so as to realize multidimensionality and high efficiency of honey source update. At the stage of onlooker bee and scout bee, crossover operator and optimal preservation strategy were introduced respectively to further break local optimality, and effectively improved convergence effect of artificial bee colony algorithm. Honey source was initialized by binary, which made the algorithm perform well in the process of feature selection. Experiments on four Benchmark test functions showed that the optimization accuracy and convergence speed of the new algorithm were better than four classical search algorithms. At the same time, seven commonly used high-dimensional data sets were selected for feature selection and the results were compared with seven classical dimensionality reduction algorithms. It was found that the feature reduction degree of the new algorithm was generally above 88%. With the higher dimension of the data set, the dimensionality reduction degree and classification accuracy of the new algorithm were better than those of the other seven dimensionality reduction algorithms.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2021539
中图分类号:TP18
引用信息:
[1]刘拥民,王靖枫,黄浩等.模拟人工蜂群的高维数据特征选择算法研究[J],2023,55(03):57-64.DOI:10.13705/j.issn.1671-6841.2021539.
基金信息:
国家自然科学基金项目(31870532);; 湖南省自然科学基金项目(2021JJ31163);; 湖南省教育科学“十三五”规划基金项目(XJK20BGD048)