| 基于支持向量机的特征提取方法研究与应用 |
| |
| 引用本文： | 蒋琳,彭黎.基于支持向量机的特征提取方法研究与应用[J].计算机工程与应用,2007,43(20):210-213. |
| |
| 作者姓名： | 蒋琳 彭黎 |
| |
| 作者单位： | 1. 湖南商学院,长沙,410205;湖南大学,软件学院,长沙,410082
2. 湖南大学,软件学院,长沙,410082 |
| |
| 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。
|
| 关 键 词： | 支持向量机 特征提取 分类识别 Ⅱ型糖尿病 |
| 文章编号： | 1002-8331（2007）20-0210-04 |
| 修稿时间： | 2006-11 |
Study of feature selection method based on support vector machine and its application |
| |
| JIANG Lin,PENG Li.Study of feature selection method based on support vector machine and its application[J].Computer Engineering and Applications,2007,43(20):210-213. |
| |
| Authors: | JIANG Lin PENG Li |
| |
| Affiliation: | 1.Hunan Business College,Changsha 410205,China; 2.Software School of Hunan University,Changsha 410082,China |
| |
| Abstract: | 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. |
| |
| Keywords: | SVM feature selection classification typeⅡdiabetes |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
|
