Table 4 |
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|
A Comparison of Structural Error for the suboptimal learning algorithm and the optimal learning algorithm |
||||||||||||
|
Greedy Hill Climbing |
Optimal |
|||||||||||
|
|
||||||||||||
|
GoldNet |
Size |
Score |
Add |
Delete |
Rev |
Mis |
Total |
Add |
Delete |
Rev |
Mis |
Total |
|
|
||||||||||||
|
Austr |
200 |
AIC |
16 |
14 |
1 |
1 |
32 |
11 |
6 |
2 |
2 |
21 |
|
200 |
MDL |
9 |
17 |
0 |
0 |
26 |
0 |
8 |
1 |
4 |
13 |
|
|
200 |
fNML |
11 |
16 |
0 |
1 |
28 |
20 |
7 |
0 |
4 |
31 |
|
|
200 |
0.1 |
7 |
17 |
0 |
1 |
25 |
0 |
10 |
0 |
4 |
14 |
|
|
200 |
0.5 |
9 |
17 |
0 |
0 |
26 |
1 |
9 |
1 |
3 |
14 |
|
|
200 |
1 |
9 |
17 |
0 |
0 |
26 |
1 |
9 |
1 |
3 |
14 |
|
|
200 |
5 |
11 |
12 |
2 |
2 |
27 |
5 |
6 |
1 |
6 |
18 |
|
|
200 |
10 |
14 |
14 |
0 |
2 |
30 |
8 |
7 |
2 |
4 |
21 |
|
|
600 |
AIC |
18 |
15 |
1 |
0 |
34 |
7 |
1 |
0 |
0 |
8 |
|
|
600 |
MDL |
13 |
15 |
1 |
0 |
29 |
0 |
2 |
0 |
0 |
2 |
|
|
600 |
fNML |
13 |
15 |
2 |
0 |
30 |
1 |
3 |
0 |
7 |
11 |
|
|
600 |
0.1 |
11 |
15 |
1 |
1 |
28 |
0 |
4 |
0 |
1 |
5 |
|
|
600 |
0.5 |
12 |
15 |
1 |
1 |
29 |
0 |
3 |
0 |
1 |
4 |
|
|
600 |
1 |
12 |
15 |
1 |
1 |
29 |
0 |
3 |
0 |
1 |
4 |
|
|
600 |
5 |
14 |
14 |
1 |
4 |
33 |
1 |
2 |
0 |
0 |
3 |
|
|
600 |
10 |
15 |
15 |
0 |
3 |
33 |
4 |
3 |
1 |
9 |
17 |
|
|
1000 |
AIC |
18 |
13 |
1 |
0 |
32 |
7 |
0 |
1 |
0 |
8 |
|
|
1000 |
MDL |
15 |
15 |
1 |
0 |
31 |
0 |
0 |
0 |
0 |
0 |
|
|
1000 |
fNML |
16 |
15 |
0 |
3 |
34 |
2 |
1 |
1 |
8 |
12 |
|
|
1000 |
0.1 |
15 |
15 |
1 |
0 |
31 |
0 |
0 |
0 |
0 |
0 |
|
|
1000 |
0.5 |
15 |
15 |
1 |
0 |
31 |
0 |
0 |
0 |
0 |
0 |
|
|
1000 |
1 |
15 |
15 |
1 |
0 |
31 |
0 |
0 |
0 |
0 |
0 |
|
|
1000 |
5 |
17 |
15 |
2 |
1 |
35 |
2 |
0 |
4 |
6 |
12 |
|
|
1000 |
10 |
18 |
15 |
2 |
1 |
36 |
4 |
1 |
1 |
8 |
14 |
|
|
|
||||||||||||
|
Crx |
200 |
AIC |
20 |
14 |
0 |
2 |
36 |
9 |
2 |
4 |
3 |
18 |
|
200 |
MDL |
9 |
16 |
0 |
3 |
28 |
1 |
8 |
0 |
9 |
18 |
|
|
200 |
fNML |
16 |
15 |
1 |
1 |
33 |
19 |
5 |
6 |
4 |
34 |
|
|
200 |
0.1 |
6 |
16 |
0 |
3 |
25 |
1 |
11 |
0 |
6 |
18 |
|
|
200 |
0.5 |
10 |
16 |
0 |
3 |
29 |
1 |
8 |
0 |
9 |
18 |
|
|
200 |
1 |
9 |
15 |
0 |
4 |
28 |
1 |
7 |
0 |
10 |
18 |
|
|
200 |
5 |
13 |
14 |
1 |
2 |
30 |
5 |
6 |
3 |
5 |
19 |
|
|
200 |
10 |
19 |
14 |
2 |
0 |
35 |
9 |
4 |
3 |
3 |
19 |
|
|
600 |
AIC |
21 |
14 |
0 |
0 |
35 |
8 |
1 |
2 |
0 |
11 |
|
|
600 |
MDL |
14 |
16 |
0 |
0 |
30 |
1 |
3 |
1 |
0 |
5 |
|
|
600 |
fNML |
14 |
14 |
0 |
4 |
32 |
3 |
3 |
1 |
7 |
14 |
|
|
600 |
0.1 |
11 |
15 |
0 |
1 |
27 |
2 |
6 |
2 |
1 |
11 |
|
|
600 |
0.5 |
13 |
15 |
0 |
0 |
28 |
1 |
3 |
1 |
0 |
5 |
|
|
600 |
1 |
13 |
15 |
0 |
0 |
28 |
1 |
3 |
1 |
0 |
5 |
|
|
600 |
5 |
17 |
13 |
2 |
3 |
35 |
6 |
2 |
2 |
7 |
17 |
|
|
600 |
10 |
18 |
13 |
0 |
3 |
34 |
8 |
3 |
2 |
6 |
19 |
|
|
1000 |
AIC |
21 |
15 |
0 |
0 |
36 |
7 |
1 |
1 |
0 |
9 |
|
|
1000 |
MDL |
14 |
15 |
1 |
0 |
30 |
1 |
2 |
1 |
1 |
5 |
|
|
1000 |
fNML |
17 |
15 |
0 |
4 |
36 |
2 |
2 |
0 |
9 |
13 |
|
|
1000 |
0.1 |
14 |
15 |
0 |
0 |
29 |
1 |
3 |
1 |
1 |
6 |
|
|
1000 |
0.5 |
13 |
15 |
0 |
0 |
28 |
1 |
3 |
1 |
1 |
6 |
|
|
1000 |
1 |
13 |
15 |
0 |
0 |
28 |
1 |
3 |
1 |
1 |
6 |
|
|
1000 |
5 |
17 |
15 |
0 |
0 |
32 |
4 |
2 |
0 |
11 |
17 |
|
|
1000 |
10 |
18 |
14 |
2 |
4 |
38 |
6 |
2 |
1 |
8 |
17 |
|
|
|
||||||||||||
|
This table gives detailed information about the structural differences between the learned and gold standard networks for the Statlog (Australian Credit Approval) and Credit Approval datasets. It shows differences for both the greedy hill climbing and the optimal learning algorithm. GoldNet gives the name of the network. Size gives the sample size. Score gives the scoring function. When only a number is shown, the scoring function is BDeu with that value for α. Add gives the number of edges that were added to the learned network that were not in the gold standard network. Delete gives the number of edges that were not in the learned network which were in the gold standard network. Rev gives the number of edges that were oriented in the wrong direction in the equivalence class of the learned network compared to that of the gold standard network; that is, the number of edges that were reversed. Mis gives the number of edges that were either directed in the equivalence class of the learned network and undirected in that of the gold standard network, or vice versa; that is, it gives the number of mis-directed edges. |
||||||||||||
|
Liu et al. BMC Bioinformatics 2012 13(Suppl 15):S14 doi:10.1186/1471-2105-13-S15-S14 |
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