计算机应用与软件Issue(5)：155-158,176,5.DOI:10.3969/j.issn.1000-386x.2014.05.040
一种基于关联规则与支持向量机的基因表达数据分类模型
A GENE EXPRESSION DATA CLASSIFICATION MODEL BASED ON ASSOCIATION RULES AND SUPPORT VECTOR MACHINE
摘要
Abstract
The discovery of association rules correlated to specific diseases through studying the gene expression data is of great importance to the computer aided diseases diagnoses.Here we propose an association rule-based gene expression data classification model (ASSO-SVM) aiming at the deficiency of interpretability in existing classification results.In this model,association rules are used as a kind of feature selection approach to extract the nonlinear associations among the genes.The priori knowledge acquired by these nonlinear associations benefits the improvement on interpretability of the classification results.Besides,in light of the features of high dimensionality and small sample of the gene expression data,the method uses support vector machine as the classifier for gene expression data to achieve higher classification accuracy.The ASSO-SVM combines the advantages of the gene expressive association rules and the SVM classification.Contrastive experiment on practical gene expression datasets in comparison with existing classification model verifies the effectiveness of the method.关键词
关联规则/基因表达数据/支持向量机/疾病辅助诊断Key words
Association rules/Gene expression data/Support vector machine/Computer aided diseases diagnoses分类
信息技术与安全科学引用本文复制引用
王美华,苏雄斌,蔡瑞初,罗静..一种基于关联规则与支持向量机的基因表达数据分类模型[J].计算机应用与软件,2014,(5):155-158,176,5.基金项目
广东省科技计划项目（2010B080701070，2012B010100029）。 （）
