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Table 3 Parameter selection to obtain the results of Table 2 using the Weka plug-in

From: Application of an interpretable classification model on Early Folding Residues during protein folding

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
  1. Classification accuracy (CA), weighted classification accuracy (WCA) with weights 0.75 and 0.25 as well as F β-measure with β=1 (F1)
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