Parameter
|
Run 1
|
Run 2
|
Run 3
|
Run 4
|
Run 5
|
Run 6
|
---|
Cost function to optimize
|
CA
|
WCA
|
F1
|
CA
|
WCA
|
F1
|
Number of epochs
|
150
|
150
|
150
|
250
|
250
|
250
|
Number of prototypes
|
1
|
1
|
1
|
5
|
5
|
5
|
Data point ratio per round
|
0.75
|
0.75
|
0.75
|
0.75
|
0.75
|
0.75
|
Sigmoid sigma interval
|
[1.0,5.0]
|
[1.0,15.0]
|
[1.0,50.0]
|
[1.0,5.0]
|
[1.0,15.0]
|
[1.0,50.0]
|
Prototype learning rate
|
1.0
|
1.0
|
1.0
|
1.0
|
1.0
|
1.0
|
Matrix learning
|
True
|
True
|
True
|
True
|
True
|
True
|
Omega learning rate
|
1.0
|
1.0
|
1.0
|
1.0
|
1.0
|
1.0
|
Omega dimension
|
27
|
27
|
27
|
27
|
27
|
27
|
Cost function beta
|
-
|
-
|
1
|
-
|
-
|
1
|
Cost function weights
|
-
|
[0.75,0.25]
|
-
|
-
|
[0.75,0.25]
|
-
|
Parallel execution
|
True
|
True
|
True
|
True
|
True
|
True
|
- Classification accuracy (CA), weighted classification accuracy (WCA) with weights 0.75 and 0.25 as well as F β-measure with β=1 (F1)