| 粒计算在基因微阵列数据特征选择中的应用 |
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| 引用本文： | 王俊,祁云嵩,韩利.粒计算在基因微阵列数据特征选择中的应用[J].科学技术与工程,2009,9(6). |
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| 作者姓名： | 王俊 祁云嵩 韩利 |
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| 作者单位： | 江苏科技大学计算机科学与工程学院,镇江,212003 |
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| 摘 要： | 对于许多模式识别问题来说,特征选择是一个非常重要的数据预处理技术,这对于维数高,而样本又相对较小的微阵列数据来说更是如此.提出一种将粒计算与传统的SVM-RFE算法相结合的特征选择算法.这种算法能够有效地去除大部分与分类无关的基因;并且能够搜索到基因数量相对较少而分类能力相对较强的信息基因子集.
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| 关 键 词： | 微阵列基因表达数据 特征选择 粒计算 SVM-RFE算法 |
Application of Granular Computing in Microarray Gene Expression Data |
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| WANG Jun,QI Yun-song,HANG Li.Application of Granular Computing in Microarray Gene Expression Data[J].Science Technology and Engineering,2009,9(6). |
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| Authors: | WANG Jun QI Yun-song HANG Li |
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| Institution: | College of Computer Science and Engineering;Jiangsu University of Science and Technology;Zhenjiang 212003;P.R.China |
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| Abstract: | Feature selection is an important preprocessing technique for many pattern recognition problems.When the number of features is very large while the number of samples is relatively small as in the microarray data analysis,feature selection is even more important.A feature selection algorithm based on a granular computing and SVM-RFE hybrid algorithm can effectively eliminate most of the irrelevant genes,and can find a more informative gene subset in which the number of informative genes is almost least but i... |
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| Keywords: | microarray data set feature selection granular computing SVM-RFE algorithm |
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