基于DE-CStacking集成的基因表达数据分类算法
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摘要
从基因层面对癌症进行诊断将有效提高患者的治愈率,但癌症基因表达数据集通常存在高维、小样本、高噪声并且类别不平衡等问题,对此类数据进行分类是一项具有挑战性的任务.针对这些问题,提出一种基于差分进化的代价敏感Stacking(DE-CStacking)集成的基因表达数据分类算法,采用随机森林、K近邻、朴素贝叶斯作为Stacking集成的初级学习器,将代价敏感的支持向量机作为次级学习器,初级学习器的输出类概率和原始特征集作为次级学习器的输入,并采用差分进化对这些学习器的参数进行优化.通过在四个UCI的癌症基因数据上的实验对比,相对于其他传统的集成算法,DE-CStacking算法在癌症基因数据上表现出更好的泛化性能.
The diagnosis of cancer on the gene level will effectively improve the cure rate of the patients. However,it is a challenging task to classify the cancer gene expression data,such as high dimension,small sample size,high noise and class-imbalance. The differential evolution based on cost-sensitive Stacking ensemble (DE-CStacking) for cancer gene expression data classification is proposed.Random Forest,K-nearest neighbors and Na?ve Bayes are used as low er-level learners of Stacking ensemble,and the cost-sensitive Support vector machine is used as the high-level learner. The original feature sets and the output class probabilities of the low er-level learners are used as the input of the high-level learner. The parameters of these learners are optimized by differential evolution. By comparing with the experimental data on four UCI cancer gene expression data,the DE-CStacking algorithm shows better generalization performance on cancer gene expression data than other traditional ensemble algorithms.
The diagnosis of cancer on the gene level will effectively improve the cure rate of the patients. However,it is a challenging task to classify the cancer gene expression data,such as high dimension,small sample size,high noise and class-imbalance. The differential evolution based on cost-sensitive Stacking ensemble (DE-CStacking) for cancer gene expression data classification is proposed.Random Forest,K-nearest neighbors and Na?ve Bayes are used as low er-level learners of Stacking ensemble,and the cost-sensitive Support vector machine is used as the high-level learner. The original feature sets and the output class probabilities of the low er-level learners are used as the input of the high-level learner. The parameters of these learners are optimized by differential evolution. By comparing with the experimental data on four UCI cancer gene expression data,the DE-CStacking algorithm shows better generalization performance on cancer gene expression data than other traditional ensemble algorithms.
引文
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[15]Mao A,Luo J,Li Y,et al.Knitted fabrics design and manufacture:a novel CAD system for qualifying bagging performance based on geometric-mechanical models[J].Computer-Aided Design,2016,75(C):61-75.
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[19]Yang Y H,Xu X B,He S B,et al.Cluster-based niching differential evolution algorithm for optimizing the stable structures of metallic clusters[J].Computational M aterials Science,2018,149:416-423.
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[22]Fujino A,Isozaki H,Suzuki J.Multi-label text categorization with model combination based on F1-score maximization[C]//Proceedings of Ijcnlp,2013:823-828.