基于多种相关性度量的特征选择方法研究
Feature Selection Algorithm Based on Multiple Correlation Measures
查看参考文献22篇
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文摘
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当前的数据挖掘和机器学习技术面临着大样本、高维度数据的挑战,使用特征选择方法作为重要的降维手段得到了极大的关注.然而,许多过滤式特征选择方法仅使用一种相关性度量去除冗余特征和不相关特征,并且没有考虑特征之间的交互性.因此,提出基于多种相关性度量的过滤式特征选择算法,另外,本文提出的算法也考虑了特征之间的交互性.该算法将转化为0-1标准形式的两种相关性度量进行融合,同时引入待选特征与已选特征的补充相关性因子解决特征之间的交互性.基于8个UCI数据集和3个常用分类器的实验验证了本文算法的有效性,同时与五种典型的过滤式特征选择方法相比,本文所提出的方法获得了更好的分类结果. |
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其他语种文摘
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Data mining and machine learning techniques are currently faced with challenges of large-sized and high-dimensional data. Using feature selection method as an important dimension reduction mean has attracted significant attention. However,many existing filter feature selection methods eliminate redundancy and irrelevance by using single correlation measure,and ignore the interaction between features. In this paper,the idea of using multiple correlation measures is adopted for filter feature selection,additionally,the proposed method is also take feature interaction into account. Two correlation measures that converted to the standard form of 0-1 are fused together in the proposed algorithm,while introducing an item to identify complementary correlation between candidate feature and selected features. To illustrate the effectiveness of the proposed method,experiments are developed based on three common classifiers and eight UCI datasets. Classification results verify the superiority of the proposed method compared with five representative filter feature selection methods. |
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来源
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小型微型计算机系统
,2017,38(4):696-700 【扩展库】
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关键词
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特征选择
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过滤式
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相关性
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交互特征
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地址
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中国科学院沈阳自动化研究所, 中国科学院网络化控制系统重点实验室, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-1220 |
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学科
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自动化技术、计算机技术 |
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基金
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辽宁省科技计划项目
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文献收藏号
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CSCD:5964404
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22
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