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:2019,28(7):174-179
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融合序列后向选择与支持向量机的混合式特征选择算法
(1.武夷学院 数学与计算机学院, 武夷山 354300;2.
认知计算与智能信息处理福建省高校重点实验室, 武夷山 354300)
Hybrid Feature Selection Algorithm for Fusion Sequence Backward Selection and Support Vector Machine
(1.School of Mathematics and Computer Science, Wuyi University, Wuyishan 354300,China;2.
Fujian Provincial Key Laboratory of Cognitive Computing and Intelligent Information Processing, Wuyishan 354300, China)
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投稿时间:2018-10-26    修订日期:2018-11-19
中文摘要: 维度灾难是机器学习任务中的常见问题,特征选择算法能够从原始数据集中选取出最优特征子集,降低特征维度.提出一种混合式特征选择算法,首先用卡方检验和过滤式方法选择重要特征子集并进行标准化缩放,再用序列后向选择算法(SBS)与支持向量机(SVM)包裹的SBS-SVM算法选择最优特征子集,实现分类性能最大化并有效降低特征数量.实验中,将包裹阶段的SBS-SVM与其他两种算法在3个经典数据集上进行测试,结果表明,SBS-SVM算法在分类性能和泛化能力方面均具有较好的表现.
Abstract:Dimensional disaster is a common problem in machine learning tasks. The feature selection algorithm can select the optimal feature subset from the original data set and reduce the feature dimension. A hybrid feature selection algorithm is proposed. Firstly, the chi-square test and filtering method are used to select the important feature subsets and normalize scale, and then SBS-SVM wrapped by SBS and SVM. The algorithm selects the optimal feature subset to maximize the classification performance and effectively reduce the number of features. In the experiment, the SBS-SVM in the parcel stage and the other two algorithms are tested on three classical data sets. The results show that the SBS-SVM algorithm has better performance in classification performance and generalization ability.
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基金项目:福建省自然科学基金(2019J01835,2017J01651,2017J01780);福建省中青年教师教育科研项目(JAT170608);认知计算与智能信息处理福建省高校重点实验室开放课题(KLCCⅡP2017104)
引用文本:
吴清寿,刘长勇,林丽惠.融合序列后向选择与支持向量机的混合式特征选择算法.计算机系统应用,2019,28(7):174-179
WU Qing-Shou,LIU Chang-Yong,LIN Li-Hui.Hybrid Feature Selection Algorithm for Fusion Sequence Backward Selection and Support Vector Machine.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):174-179

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