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 |