bioinformatics; diseases; genetics; medical computing; pattern classification; ReliefF method; bioinformatics; cancer datasets; cell samples; disease diagnosis; disease prediction; filter approach; filter based gene selection; gene dataset experiments; gene expression measurements; high dimensional gene expression dataset; leukemia; lymphomas; maximum classification accuracy; optimum gene selection; ovarian cancer; public domain datasets; random gene subset selection algorithm; supervised classifiers; time management; tissue samples; wrapper approach; Accuracy; Algorithm design and analysis; Bioinformatics; Classification algorithms; Niobium; Prediction algorithms; Support vector machines; Ensemble classifier; Gene selection; Linear classifier; Random gene subset selection; ReliefF;
机译:一种新颖的滤波器包装器混合贪婪集合方法,使用遗传算法优化,以减少高维生物医学数据集的维度
机译:基于高维数据集的基于高效的混合滤波器包装成殖民培养方法
机译:高维癌症微阵列数据集特征选择的嵌套遗传算法
机译:响应理论(1RT),人机交互(HC1)。卵巢癌微阵列数据集遗传途径分析的混合过滤器和包装器算法
机译:后处理包装程序生成的表用于标记匿名数据集。
机译:提取多个基因表达数据集的低维描述揭示了卵巢癌中肿瘤相关基质的潜在驱动因素
机译:提取多个基因表达数据集的低维描述揭示了卵巢癌中肿瘤相关基质的潜在驱动因素