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Table 6 LOOCV accuracy and the number of genes used in classifiers (in parenthesis) for binary class expression datasets

From: TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection

Method Colon Leuk CNS DLBCL Lung Pros1 Pros2 Pros3 GCM Aver
TSG 93.55
(2)
98.61
(2)
97.06
(2)
98.7
(2)
100
(2)
95.1
(2)
86.36
(10)
100
(2)
87.5
(7)
95.21
TSP* 91.10
(2)
93.80
(2)
77.90
(2)
98.10
(2)
98.30
(2)
95.10
(2)
67.60
(2)
97.00
(2)
75.40
(2)
88.26
k-TSP* 90.30
(2)
95.83
(18)
97.10**
(10)
97.40
(2)
98.90
(10)
91.18
(2)
75.00
(18)
97.00
(2)
85.40
(10)
92.01
DT* 77.42
(3)
73.61
(2)
67.65
(2)
80.52
(3)
96.13
(3)
87.25
(4)
64.77
(4)
84.85
(1)
77.86
(14)
78.90
NB* 56.45 100 82.35 80.52 97.79 62.75 73.86 90.91 84.29 80.99
k-NN* 74.19 84.72 82.35 89.61 98.34 74.51 73.86 93.94 86.79 84.26
SVM* 82.26 98.61 82.35 97.40 99.45 91.18 76.14 100 93.21 91.18
PAM* 89.52
(15)
94.03
(2296)
82.35
(4)
85.45
(17)
97.90
(9)
90.89
(47)
81.25
(13)
94.24
(701)
82.32
(47)
88.66
  1. *Results reported in Tan et al. [3]Results obtained with our method (TSG) NB, k-NN, SVM used entire set of genes
  2. **The 97.10 reported in Tan et al. [3] may be a result of rounding 97.06, which is the accuracy of correctly classifying 33 of the 34 samples.