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Table 1 Comparisons of seven classification methods on simulated data

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

Methods ISSC ISDC DSSC DSDC
DLDA 0.048 (0.015, 50) 0.083 (0.015, 100) 0.228 (0.02, 1025) 0.217 (0.04, 1175)
DQDA 0.049 (0.021, 50) 0.013 (0.007, 50) 0.243 (0.025, 1400) 0.214 (0.032, 825)
NN 0.056 (0.02, 100) 0.424 (0.021, 50) 0.27 (0.034, 575) 0.112 (0.061, 475)
SVM 0.054 (0.029, 50) 0.095 (0.024, 100) 0.127 (0.047, 500) 0.255 (0.05, 1050)
SCRDA 0.019 (0.036, 651) 0.024 (0.012, 2089) 0.217 (0.041, 587) 0.241 (0.069, 317)
RF 0.109 (0.012, NA) 0.038 (0.009, NA) 0.262 (0.018, NA) 0.21 (0.041, NA)
SQDA 0.005 (0.002, 300) 0.001 (0.001, 300) 0.108 (0.042, 200) 0.001 (0.002, 400)
DLDA2 0.002 (0.001, 600) 0.04 (0.005, 500) 0.224 (0.03, 700) 0.217 (0.055, 600)
DQDA2 0.003 (0.001, 500) 0 (0.001, 600) 0.231 (0.033, 600) 0.224 (0.058, 400)
  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.