基于离散粒子群和支持向量机的特征基因选择算法

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基因芯片表达谱信息,为识别疾病相关基因及对癌症等疾病分型、诊断及病理学研究提供一新途径.在基因表达谱数据中选择特征基因可以提高疾病诊断、分类的准确率,并降低分类器的复杂度.本文研究了基于离散粒子群(binary particle swarm optimization,BPSO)和支持向量机(support vector machine,SVM)封装模式的BPSO-SVM特征基因选择方法,首先随机产生若干种群(特征子集),然后用BPSO算法优化随机产生的特征基因,并用SVM分类结果指导搜索,最后选出最佳适应度的特征基因子集以训练SVM.结果表明,基于BPSO-SVM的特征基因选择方法,的确是一种行之有效的特征基因选择方法.
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