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Table 7 Parameters used in the experiments.

From: A comparison of machine learning techniques for survival prediction in breast cancer

GP Parameters
population size 500 individuals
population initialization ramped half and half [26]
selection method tournament (tournament size = 10)
crossover rate 0.9
mutation rate 0.1
maximum number of generations 5
algorithm generational tree based GP with no elitism
SVM Parameters
complexity parameter 0.1
size of the kernel cache 107
epsilon value for the round-off error 10-12
exponent for the polynomial kernel 1.0,2.0, 3.0
tolerance parameter 0.001
Multilayered Perceptron Parameters
learning algorithm Back-propagation
learning rate 0:03
activation function for all the neurons in the net sigmoid
momentum 0.2 progressively decreasing until 0.0001
hidden layers (number of attributes + number of classes)/2
number of epochs of training 500
Random Forest Parameters
number of trees 2500
number of attributes per node 1