Feature extraction and classification of tumor based on wavelet package and support vector machines

被引：0
|
作者：
Wang, Shulin [1 ,2 ]
Wang, Ji [1 ]
Chen, Huowang [1 ]
Li, Shutao [3 ]
机构：
[1] Natl Univ Defense Technol, Sch Comp Sci, Changsha 410073, Peoples R China
[2] Hunan Univ, Sch Comp & Commun, Changsha 410082, Hunan, Peoples R China
[3] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
来源：
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2007年 / 4426卷
关键词：
gene expression profiles; tumor classification; feature extraction; support vector machines; wavelet package decomposition;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
DNA microarray experiments provide us with huge amount of gene expression data, which leads to a dimensional disaster for extracting features related to tumor. A wavelet package decomposition based feature extraction method for tumor classification was proposed, by which eigenvectors are extracted from gene expression profiles and used as the input of support vector machines classifier. Two well-known datasets are examined using the novel feature extraction method and support vector machines. Experiment results show that the 4-fold cross-validated accuracy of 100% is obtained for the leukemia dataset and 93.55% for the colon dataset.
引用
收藏
页码：871 / +
页数：3
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