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基于支持向量机的特征提取方法研究与应用 被引量:10

Study of feature selection method based on support vector machine and its application
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摘要 支持向量机是一种基于结构风险最小化原理的分类技术,已逐渐引起国内外研究者的关注。提出了一种用于最佳特征子集选取的特征筛选算法,且实现了特征与分类识别相关性强度的排序,并通过使用该算法对Ⅱ型糖尿病判别与风险因素筛选,求证了该方法的可靠性和可行性。当以该算法提取的特征子集{腰围、腰围/臀围、舒张血压、年龄}作为输入向量时,敏感度、特异性、准确率最高,分别为0.8666、0.6420、0.7014。同时,还将该算法与主成分分析法进行比较。实验表明,在特征提取方面该算法优于主成分分析法。因此,该算法对分类识别、风险因素筛选是一种有效的方法,为解决该类问题探索了一条有效途径。 Support Vector Machine(SVM),a kind of machine learning method,can efficiently solve the classification problem.A new classification-based feature selection algorithm is developed in this study.This algorithm is able to explore the best subset of features for classification from a group of either irrelevant or relevant features.Moreover,it can systematically prioritize all features based on degree of correlation between them and categories.And it finally is used to identify a set of combined-risk factors for type II diabetes in this study.A best subset of risk factors,consisting of waistline,waistline/hip-girth,diastolic blood pressure and age,is found for this disease.The sensitivity,specificity and accuracy of SVM classification under this subset are 0.866 6,0.642 0 and 0.701 4 respectively.In addition,a comparison between this algorithm and principal component analysis is also conducted.It turns out that the former is superior to the latter for the extraction of features.
作者 蒋琳 彭黎
出处 《计算机工程与应用》 CSCD 北大核心 2007年第20期210-213,共4页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60473031)
关键词 支持向量机 特征提取 分类识别 Ⅱ型糖尿病 SVM feature selection classification type Ⅱ diabetes
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参考文献4

  • 1Vapnik V.The nature of statistical learning theory[M].New York:Springer-Verlag,1999:1-226.
  • 2何晨光,杜丽芳.Ⅱ型糖尿病相关因素的Meta-分析[J].口岸卫生控制,2001,6(5):21-23. 被引量:2
  • 3Zhang Jian-zhong,Xu Shao-ji.Linear programming[M].Beijing:Science Press,1997.
  • 4Schlkopf B,Mika S.Input space vs feature space in kernel based methods[J].IEEE Trans on Neural Networks,1999,10(9):1000-1017.

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