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Table 2 Comparisons of seven classification methods on TCGA data

From: The application of sparse estimation of covariance matrix to quadratic discriminant analysis

Methods LUSC LUAD TC
DLDA 0.013 (0.007, 50) 0.035 (0.027, 50) 0.1 (0.042, 50)
DQDA 0.008 (0.006,50) 0.018 (0.019, 50) 0.08 (0.047, 50)
NN 0.014 (0.01, 50) 0.027 (0.016, 50) 0.085 (0.053, 50)
SVM 0.01 (0.007, 50) 0.024 (0.017, 50) 0.088 (0.041, 50)
SCRDA 0.039 (0.031, 128) 0.044 (0.026, 95) 0.122 (0.068, 502)
RF 0.007 (0.002, NA) 0.018 (0.009, NA) 0.08 (0.039, NA)
SQDA 0.003 (0.003, 1900) 0.011 (0.009, 1900) 0.036 (0.021, 2900)
DLDA2 0.017 (0.005, 12100) 0.031 (0.013, 8900) 0.114 (0.038, 2300)
DQDA2 0.008 (0.004,10000) 0.023 (0.008, 8300) 0.107 (0.05, 5800)
Methods PC HNC LC
DLDA 0.125 (0.024, 50) 0.034 (0.012, 50) 0.055 (0.017, 50)
DQDA 0.11 (0.022, 50) 0.03 (0.016, 50) 0.045 (0.021, 50)
NN 0.094 (0.029, 50) 0.032 (0.013, 50) 0.051(0.015, 50)
SVM 0.116 (0.031, 150) 0.037 (0.023, 50) 0.04 (0.014, 50)
SCRDA 0.094 (0.037, 1989) 0.039 (0.021, 2200) 0.069 (0.026, 56)
RF 0.11 (0.013, NA) 0.033 (0.013, NA) 0.048 (0.018, NA)
SQDA 0.206 (0.134, 1300) 0.021 (0.015, 2200) 0.04 (0.041, 500)
DLDA2 0.128 (0.026, 3400) 0.033 (0.01, 6600) 0.068 (0.02, 7800)
DQDA2 0.205 (0.066, 3100) 0.049 (0.022, 5900) 0.089 (0.027, 6100)
Methods BC KC CC
DLDA 0.035 (0.017, 50) 0.028 (0.018, 50) 0.006 (0.008, 50)
DQDA 0.018 (0.009, 50) 0.037 (0.03, 50) 0.004 (0.006, 50)
NN 0.021 (0.013, 50) 0.031 (0.019, 50) 0.005 (0.009, 50)
SVM 0.018 (0.012, 50) 0.028 (0.018, 50) 0.004 (0.006, 50)
SCRDA 0.045 (0.019, 452) 0.047 (0.011, 78) 0.023 (0.014, 49)
RF 0.027 (0.013, NA) 0.025 (0.014, NA) 0.011 (0.011, NA)
SQDA 0.021 (0.008, 2800) 0.009 (0.005, 6100) 0.007 (0.008, 5900)
DLDA2 0.036 (0.015, 8000) 0.039 (0.007, 10200) 0.02 (0.014, 11700)
DQDA2 0.069 (0.033, 7400) 0.045 (0.035, 9600) 0.021 (0.014, 10200)
  1. The reported numbers in each table entry in the form of a (b,c) mean: a is the average prediction error, b is the standard deviation, and c is the median number of predictors selected.