Description Usage Arguments Value Examples
View source: R/standardgeneticalgorithm.R
Standard Genetic Algorithm. Implements a standard genetic algorithm using GA package (ga) with a fitness function specialised for feature selection.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
model |
The ML models used to classify the data, typically SVM with a given kernel |
k |
Maximum number of features to be output. |
training |
Training dataset as a data.frame with classification column and column for each feature. |
test |
Test dataset with matching columns to training. |
parallel |
Specifies whether GA should be run sequentially or in parallel (default: TRUE) |
mutprob |
The probability that an individual undergoes mutation in a particular iteration (default: 0.1) |
crossprob |
The probability of crossover between pairs of individuals (default: 0.8) |
popsize |
The size of the solution population (default:20) |
maxiter |
The maximum number of iterations to run before termination (default: 1000) |
maxiter_withoutimprovement |
The maximum number of consecutive iterations without improvement to fitness before termination (default: 300) |
numberpassedon |
The number of best fitness individuals to be passed on to the next generation in each iteration (default: 3) |
plot |
Specifies whether GA plot should be shown (default: FALSE) |
Set (unordered) of <=k features and training and test accuracy, sensitivity and specificity using these features.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | data_train = data.frame(
classification=as.factor(c(1,1,0,0,1,1,0,0,1,1)),
A=c(1,1,1,0,0,0,1,1,1,0),
B=c(0,1,1,0,1,1,0,1,1,0),
C=c(0,0,1,0,0,1,0,0,1,0),
D=c(0,1,1,0,0,0,1,0,0,0),
E=c(1,0,1,0,0,1,0,1,1,0))
data_test = data.frame(
classification=as.factor(c(1,1,0,0,1,1,1,0)),
A=c(0,0,0,1,0,0,0,1),
B=c(1,1,1,0,0,1,1,1),
C=c(0,0,1,1,0,0,1,1),
D=c(0,0,1,1,0,1,0,1),
E=c(0,0,1,0,1,0,1,1))
geneticalgorithm(
feamiR::svmlinear,
k=2,
data_train,
data_test,
parallel=FALSE,
maxiter=5,
maxiter_withoutimprovement=5,
popsize=10)
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