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Table 2 Performance evaluation of the reliable negative sample extraction algorithms

From: Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach

Data setAlgorithmFPR%FNR%Precision %Recall %F_measure %
Parkinson’s DiseaseNB37.254.5795.4389.7892.52
SPY8.7016.1197.4283.8990.15
Roc-SVM6.5215.0098.0885.0091.07
Liver DisordersNB17.655.7173.3394.2982.50
SPY36.14040.0010057.14
Roc-SVM31.335.0042.2295.0058.46
CloudNB18.887.9384.8392.0788.30
SPY9.5214.9292.7785.0888.76
Roc-SVM6.3216.5196.7283.4989.62
IonosphereNB47.628.3388.5191.6790.06
SPY26.326.9894.1293.0293.57
Roc-SVM33.338.8994.2591.1192.66
MAGIC Gamma TelescopeNB10.4944.4468.1855.5661.22
SPY17.8836.2253.8863.7858.42
Roc-SVM6.6847.1877.6552.8262.87
Mammographic MassNB7.2533.7285.0766.2874.51
SPY11.9610.0062.0790.0073.47
Roc-SVM1.9528.5794.3471.4381.30
Breast Cancer WisconsinNB13.8512.2691.1887.7489.42
SPY9.0910.4894.0089.5291.71
Roc-SVM22.5022.1491.8977.8684.30
Connectionist Bench (Sonar, Mines vs. Rocks)NB13.8512.2691.1887.7489.42
SPY16.677.6980.0092.3185.71
Roc-SVM22.5022.1491.8977.8684.30