feamiR (version 0.1.0)

geneticalgorithm: Standard Genetic Algorithm. Implements a standard genetic algorithm using GA package (ga) with a fitness function specialised for feature selection.

Description

Standard Genetic Algorithm. Implements a standard genetic algorithm using GA package (ga) with a fitness function specialised for feature selection.

Usage

geneticalgorithm(
  model = feamiR::svmlinear,
  k = 30,
  training,
  test,
  parallel = TRUE,
  mutprob = 0.1,
  crossprob = 0.8,
  popsize = 20,
  maxiter = 1000,
  maxiter_withoutimprovement = 300,
  numberpassedon = 3,
  plot = FALSE
)

Arguments

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)

Value

Set (unordered) of <=k features and training and test accuracy, sensitivity and specificity using these features.

Examples

# NOT RUN {
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)
# }