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Table 2 Multi-class classification results for the class-imbalanced scenario in the alternative case

From: Improved shrunken centroid classifiers for high-dimensional class-imbalanced data

Method λ a #, % info # non-info [%] FDR PA 1 PA 2 PA 3 g-means
      (n 1  = 100) (n 1  = 20) (n 3  = 100)  
PAM 6.6 59.23 0 [0.00] 0 0.86 0.04 0.86 0.29
  (0.58) (33.68) (0) (0.00) (0.02) (0.02) (0.02) (0.05)
GM-PAM 1.4 99.64 834.56 [17.03] 0.5 0.75 0.31 0.75 0.55
  (0.96) (5.75) (1262.68) (0.41) (0.04) (0.06) (0.04) (0.04)
ALP 58.76 99.98 49 [1.00] 0.01 0.82 0.15 0.82 0.46
  (15.12) (0.14) (490) (0.1) (0.04) (0.05) (0.03) (0.04)
GM-ALP 12.77 99.64 415.68 [8.48] 0.19 0.74 0.34 0.74 0.57
  (12.88) (3.6) (1330.05) (0.31) (0.05) (0.08) (0.05) (0.05)
AHP 59.05 99.99 98 [2.00] 0.02 0.82 0.15 0.82 0.45
  (15.74) (0.1) (689.46) (0.14) (0.04) (0.05) (0.04) (0.05)
GM-AHP 13.55 100 316.73 [6.46] 0.17 0.74 0.34 0.74 0.57
  (13.05) (0) (1164.99) (0.28) (0.05) (0.08) (0.05) (0.04)
  1. The table reports the same information as Table 1; # non-info [%] was selected out of 4,900 non-informative variables and #, % info was selected out of 100 informative variables; see text for details.
  2. aFor AHP and GM-AHP only λ θ was optimized while λ γ was set to zero.