IEEE Computational Intelligence Magazine - May 2020 - 73
significantly better or significantly
worse than the compared method. The
symbol "="indicates the performance of
FLGP is similar to the compared method. In Tables III and IV, each small
block lists the results on one data set,
and the maximum classification accuracy is highlighted in bold. The final row
of each block summarizes the overall
results of the significance test.
A. Overall Classification Performance
As mentioned in Section IV-B, the GPGLF and the other four GP-based
benchmark methods are only used for
comparisons on binary classification
tasks. Thus, there are 16 benchmark
methods on FEI_1, FEI_2 and VGDB
and 11 benchmark methods on the
remaining data sets. From the final rows
of Tables III and IV, it is obvious that
FLGP achieves significantly better or
similar performance in almost all the
comparisons. Specifically, FLGP gains 97
TABLE III Classification accuracy (%) of the proposed FLGP approach and sixteen benchmark methods on three binary data sets:
FEI_1, FEI_2 and VGDB.
FEI_1
FEI_2
VGDB
METHODS
MAX
MEAN±STD.
MAX
MEAN±STD.
MAX
MEAN ± STD.
2TGP
96.0
88.1 ± 6.2 +
94.0
85.5 ± 6.0 +
63.9
61.6 ± 1.5 +
DIF + GP
80.0
56.7 ± 6.9 +
72.0
60.3 ± 8.4 +
68.7
61.4 ± 3.5 +
Hist + GP
70.0
48.9 ± 7.2 +
60.0
48.8 ± 6.1 +
84.3
76.0 ± 2.6 =
uLBP+GP
66.0
50.9 ± 7.5 +
72.0
48.7 ± 7.9 +
79.5
69.4 ± 4.4 +
GP-GLF
96.0
89.1 ± 3.9 +
92.0
82.5 ± 5.8 +
74.6
65.1 ± 4.7 +
DIF
74.0
61.1 ± 4.9 +
72.0
62.8 ± 6.1 +
66.3
55.6 ± 10.3 +
Hist
54.0
48.1 ± 3.4 +
54.0
50.1 ± 2.5 +
62.7
62.2 ± 0.8 +
GLCM
50.0
49.7 ± 0.7 +
54.0
50.1 ± 0.7 +
62.7
53.3 ± 9.8 +
Gabor
82.0
71.6 ± 7.9 +
74.0
65.7 ± 5.1 +
63.9
56.0 ± 8.3 +
SIFT
82.0
82.0 ± 0.0 +
78.0
78.0 ± 0.0 +
60.2
60.2 ± 0.0 +
HOG
94.0
94.0 ± 0.0 +
88.0
88.0 ± 0.0 +
57.8
57.2 ± 0.7 +
LBP
68.0
62.5 ± 3.5 +
66.0
57.6 ± 3.6 +
84.3
80.6 ± 3.2 -
uLBP
64.0
56.9 ± 5.2 +
56.0
51.9 ± 2.3 +
81.9
71.5 ± 8.1 =
LeNet-5
98.0
94.4 ± 2.0 =
94.0
90.8 ± 1.8 +
65.1
58.1 ± 4.8 +
CNN-5
98.0
95.6 ± 1.5 =
90.0
85.0 ± 3.0 +
65.1
61.5 ± 2.1 +
CNN-8
98.0
94.2 ± 2.1 =
94.0
90.0 ± 2.3 +
61.5
56.9 ± 4.6 +
FLGP
98.0
95.8 ± 3.2
100
93.3 ± 3.8
81.9
74.9 ± 3.7
OVERALL
13 +, 3 =
16+
13 +, 2 =, 1 -
TABLE IV Classification accuracy (%) of the proposed FLGP approach and eleven benchmark methods on five multi-class data sets:
ORL, JAFFE, KTH, EYALE, and SCENE.
ORL
METHODS
MAX
MEAN ±
STD.
JAFFE
MAX
MEAN ±
STD.
KTH
MAX
EYALE
MEAN ±
STD.
MAX
MEAN ±
STD.
SCENE
MAX
MEAN ±
STD.
DIF
85.0
85.0 ± 0.0 +
35.6
35.6 ± 0.0 +
56.2
56.2 ± 0.0 +
26.4
26.4 ± 0.0 +
33.5
33.5 ± 0.0 +
Hist
97.5
97.5 ± 0.0 +
19.2
19.2 ± 0.0 +
51.4
51.4 ± 0.0 +
11.0
11.0 ± 0.0 +
21.2
21.2 ± 0.0 +
GLCM
2.5
2.5 ± 0.0 +
15.1
15.1 ± 0.0 +
23.3
23.3 ± 0.0 +
5.1
5.0 ± 0.1 +
13.9
13.9 ± 0.0 +
Gabor
59.2
57.1 ± 0.9 +
46.6
43.2 ± 1.6 +
44.3
42.9 ± 0.7 +
36.7
36.3 ± 0.2 +
22.7
22.3 ± 0.2 +
SIFT
98.3
98.3 ± 0.0 +
74.0
74.0 ± 0.0 +
81.4
81.4 ± 0.0 +
88.4
88.4 ± 0.0 +
63.1
63.1 ± 0.0 +
HOG
96.7
96.7 ± 0.0 +
72.6
72.6 ± 0.0 +
51.4
51.4 ± 0.2 +
74.3
74.3 ± 0.0 +
30.8
30.8 ± 0.0 +
LBP
87.5
87.5 ± 0.0 +
21.9
21.9 ± 0.0 +
87.6
87.6 ± 0.0 +
46.4
46.4 ± 0.0 +
62.5
62.5 ± 0.0 +
uLBP
94.2
94.2 ± 0.0 +
23.3
23.3 ± 0.0 +
81.0
81.0 ± 0.0 +
56.1
56.1 ± 0.0 +
66.1
66.1 ± 0.0 +
LeNet
93.3
89.9 ± 1.9 +
79.5
68.9 ± 7.0 +
78.6
72.0 ± 6.4 +
92.4
89.4 ± 1.6 +
54.2
51.1 ± 2.4 +
CNN-5
97.5
96.3 ± 0.8 +
80.8
78.9 ± 1.3 +
84.3
81.5 ± 1.8 +
99.3
98.6 ± 0.5 +
58.9
55.4 ± 1.4 +
CNN-8
96.7
94.2 ± 1.8 +
61.6
52.5 ± 6.2 +
82.4
80.7 ± 1.5 +
90.9
88.2 ± 1.0 +
69.2
66.2 ± 2.0 +
FLGP
100
99.6 ± 0.7
91.8
81.1 ± 4.7
95.7
94.5 ± 0.9
99.8
99.2 ± 0.4
77.0
75.2 ± 0.7
OVERALL
11 +
11 +
11 +
11 +
11 +
MAY 2020 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
73
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