Number of the t-test based features among the top 10 best ranked features for predicting the classification error of RF, SVM, LDA and KNN (microarray dataset), see also Fig 6.
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Number of the t-test based features among the top 10 best ranked features for predicting the classification error of RF, SVM, LDA and KNN (microarray dataset), see also Fig 6.
本数据集统计了用于预测随机森林(Random Forest, RF)、支持向量机(Support Vector Machine, SVM)、线性判别分析(Linear Discriminant Analysis, LDA)与k近邻(k-Nearest Neighbor, KNN)分类误差的排名前10的最优特征中，基于t检验(t-test)的特征的数量（微阵列数据集(microarray dataset)），详见图6。
创建时间：
2022-11-09
