FSinR (version 1.0.8)

hc: Hill-Climbing

Description

The hc Russell2009FSinR method starts with a certain set of features and in each iteration it searches among its neighbors to advance towards a better solution. The method ends as soon as no better solutions are found

Usage

hc(
  data,
  class,
  featureSetEval,
  start = NULL,
  nneigh = length(data) - 1,
  repeats = 1,
  verbose = FALSE
)

Arguments

data

• A data frame with the features and the class of the examples

class

• The name of the dependent variable

featureSetEval

• The measure for evaluate features

start

• Binary vector with the set of initial features

nneigh

• Number of neighbors to evaluate in each iteration of the algorithm. By default: all posibles. It is important to note that a high value of this parameter considerably increases the computation time.

repeats

• Number of repetitions of the algorithm

verbose

• Print the partial results in each iteration

Value

A list is returned containing for each repetition of the algorithm:

bestFeatures

A vector with all features. Selected features are marked with 1, unselected features are marked with 0

bestFitness

Evaluation measure obtained with the feature selection

initialVector

The vector with which the algorithm started

initialFitness

The evaluation measure of the initial vector

trace

Matrix with the results of each iteration. It contains the number of the iteration, the best set of features selected by the algorithm up to that iteration (1: selected, 0: not selected) and the value of the evaluation measure obtained from that best set of features

References

Examples

# NOT RUN {
## Hill-Climbing method for iris dataset (filter method)
hc(iris, 'Species', roughsetConsistency)
# }