IEEE Computational Intelligence Magazine - August 2020 - 40
Noise_00
Noise_01
100
90
80
70
60
50
40
30
Score
Number of Terms−r
20
10
0
0
1
0
5
10
15
20
0
5
Noise_05
10
15
20
2
3
Noise_10
100
4
90
5
80
70
60
50
40
30
20
10
0
0
5
10
15
20
0
5
10
15
20
Number of Attributes−k
FIGURE 6 Final score grids of the heuristic using minAcc = 1 in a problem with d = 20, k = 5 and r = 20 for the 4 levels of noise.
TABLE 3 Results of the Friedman test performed to
determine the best minAcc value depending on the noise.
Control shows which was the algorithm that obtained the
best ranking. Dominance shows the configurations that are
significantly worst than the control configuration according
to the Holm post-hoc test with confidence threshold 0.05.
CONTROL
40
d
NOISE
p-VALUE
1.0
0.95
20
0%
0.00050
)
20
1%
0.00100
)
20
5%
1.895E-07
)
20
10%
1.119E-11
0.9
DOMINANCE
0.9
1.0
1.0, 0.9
)
1.0, 0.95
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2020
introduces a new hyper-parameter minAcc, the percentage of
permitted noise is a much more intuitive hyper-parameter to
set up than the coverage breakpoint, since it is an structural
hyper-parameter of the problem, instead of being a hyperparameter of the system.
One interesting aspect to notice in Table 2 is that for problems with larger k, the heuristic seems to fail when the problem has either too many or too few terms. When the problem
has too many terms, it is possible to find representatives but
these representatives are likely to have a large k and this value
might not intersect with the other two areas, as it was exemplified in Figure 6. Moreover, if the problem has few terms finding a representative becomes very difficult and it is possible that
IEEE Computational Intelligence Magazine - August 2020
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