用于医学诊断的基因选择集成和分类集成

M. Ćwiklińska-Jurkowska
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摘要

通过微阵列癌症数据集的例子来检验组合方法的有效性,其中报告了大量基因的表达水平。在与大量基因表达有关的三个数据集上,对两组的歧视问题进行了检查。对于三个被检查的微阵列数据集,在整个数据集的剩余一半上评估的交叉验证误差(之前未用于基因选择)被用作分类器性能的度量。将常见的基因选择单一程序——微阵列预测分析(PAM)和微阵列显著性分析(SAM)——与八种选择程序的融合或其中五种较小子集的融合进行比较,不包括SAM或PAM。合并五种或八种选择方法得到了类似的结果。根据三个被检测的微阵列数据集的误分类率,对于任何被检测的分类器集合,基因选择方法的组合并不优于两个被检测数据集的单个PAM或SAM选择。此外,5种基本分类器(k近邻分类器、参数c=1的支持向量机线性分类器和支持向量机径向分类器、萎缩质心正则化分类器(SCRDA)和最接近均值分类器)的异质组合过程被证明显著优于bagging决策树等重采样分类器。在某些基因数量和数据集的范围内,异构组合分类器也优于双套袋,但合并通常并不优于随机森林。对于异质或同质组合分类器的性能来说,组合基因排序的初步步骤通常不是必需的。
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Gene selection ensembles and classifier ensembles for medical diagnosis
Summary The usefulness of combining methods is examined using the example of microarray cancer data sets, where expression levels of huge numbers of genes are reported. Problems of discrimination into two groups are examined on three data sets relating to the expression of huge numbers of genes. For the three examined microarray data sets, the cross-validation errors evaluated on the remaining half of the whole data set, not used earlier for the selection of genes, were used as measures of classifier performance. Common single procedures for the selection of genes—Prediction Analysis of Microarrays (PAM) and Significance Analysis of Microarrays (SAM)—were compared with the fusion of eight selection procedures, or of a smaller subset of five of them, excluding SAM or PAM. Merging five or eight selection methods gave similar results. Based on the misclassification rates for the three examined microarray data sets, for any examined ensemble of classifiers, the combining of gene selection methods was not superior to single PAM or SAM selection for two of the examined data sets. Additionally, the procedure of heterogeneous combining of five base classifiers—k-nearest neighbors, SVM linear and SVM radial with parameter c=1, shrunken centroids regularized classifier (SCRDA) and nearest mean classifier—proved to significantly outperform resampling classifiers such as bagging decision trees. Heterogeneously combined classifiers also outperformed double bagging for some ranges of gene numbers and data sets, but merging is generally not superior to random forests. The preliminary step of combining gene rankings was generally not essential for the performance for either heterogeneously or homogeneously combined classifiers.
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