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The GA searches for the genome which maximizes f .
Algorithm 1 Single Variable Classifier algorithm (SVC)
Require:
x app : Value of the variable for the training set examples,
x test : Value of the variable for the test set examples,
y app : True labels of the training set examples,
y test : True labels of the test set examples,
nb test : Number of individuals in the test set
Ensure: Calculate a threshold T using x app and a correct classification rate (CCR)
Calculus of the threshold T
(
y app )
It calculates the mean of x app for the individuals of the first modality G 1 and the second
modality G 2 .
T
G 1 ,
G 2 )
Average
(
x app ,
G 1 +
G 2
2
Prediction and Correct classification rate
For i= 1 to nb test do
Pred i
x test )
It predicts the class of the i th individual of the test set using the threshold T .
end for
CCR
Predict
(
T
,
y test )
It calculates the correct classification rate by comparing the prediction and true labels.
return T , CCR
CalculateCCR
(
Pred
,
9.6.2.3 Stop Criterion
The algorithm stops if one of the following three conditions is satis
ed:
The number of iterations exceeds 1,000.
￿
Parents are the same for 10 generations.
￿
The number of differences among the parents is less than 3.
￿
To calculate the number of differences for a given population, denoted D , the
genomes of the population at iteration i are stored in a matrix, denoted by P i . Let
P j
be the column j of the matrix P i (where j =1,
, 16). Then D = D b + D elec where:
D b is the number of differences for the binary part of
P j
(columns 2
16). The
￿
-
P j
number of differences
for
column
(where
j =2,
, 16)
is
min ð number of 0 in P j
number of 1 in P j Þ
,
.
D elec is the number of differences in P i (column corresponding to the electrode
component). Then, D elec is the number of individuals who have a electrode
which is different from the electrode most selected in the population.
￿
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