Parameter

Run 1

Run 2

Run 3

Run 4

Run 5

Run 6


Cost function to optimize

CA

WCA

F_{1}

CA

WCA

F_{1}

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 (F_{1})