引用本文: | 刘艺,曹建军,刁兴春,等.稳定特征选择的多目标蚁群优化.[J].国防科技大学学报,2018,40(6):118-123.[点击复制] |
LIU Yi,CAO Jianjun,DIAO Xingchun,et al.Multiobjective ant colony optimization for stable feature selection[J].Journal of National University of Defense Technology,2018,40(6):118-123[点击复制] |
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稳定特征选择的多目标蚁群优化 |
刘艺1, 曹建军2, 刁兴春1, 张磊1 |
(1. 陆军工程大学 指挥信息系统学院, 江苏 南京 210007;2. 国防科技大学 第六十三研究所, 江苏 南京 210007)
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摘要: |
为了提高进化算法特征选择稳定性,提出一种面向稳定特征选择的多目标蚁群优化方法。通过抽样策略集成三种特征排序法的输出作为多目标蚁群优化的稳定性指导信息,聚合特征的费舍尔分值和最大信息系数值作为多目标蚁群优化的启发式信息,以分类正确率和扩展昆彻瓦指标值作为两个优化目标,兼顾算法的分类性能与特征选择稳定性。在四个标准数据集上进行对比实验,结果表明,所提方法能够在分类性能与稳定性方面达到较好的平衡。 |
关键词: 多目标蚁群优化 特征选择 特征选择稳定性 高维数据 |
DOI:10.11887/j.cn.201806016 |
投稿日期:2017-09-15 |
基金项目:国家自然科学基金资助项目(61371196) |
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Multiobjective ant colony optimization for stable feature selection |
LIU Yi1, CAO Jianjun2, DIAO Xingchun1, ZHANG Lei1 |
(1. College of Command Information Systems, PLA Army Engineering University, Nanjing 210007, China;2. The 63rd Institute, National University of Defense Technology, Nanjing 210007, China)
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Abstract: |
To improve the feature selection stability of evolutionary algorithms, a new method for stable feature selection based on multiobjective ant colony optimization was developed. Feature selection results of three feature ranking methods by resampling policy were combined to provide stable features′ information for multiobjective ant colony optimization; the feature′s Fisher discriminant value and maximal information coefficient value were integrated as heuristic information; the classification correctness rate and value of extensions of Kuncheva similarity measure were taken as two optimization objectives to balance algorithm′s classification performance and its stability. Some comparisons and experiments were carried out on four benchmark data sets, and results show that the proposed method has a better tradeoff between classification performance and feature selection stability. |
Keywords: multiobjective ant colony optimization feature selection stability of feature selection high dimensional data |
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