FSelector (version 0.34)

hill.climbing.search: Hill climbing search

Description

The algorithm for searching atrribute subset space.

Usage

hill.climbing.search(attributes, eval.fun)

Value

A character vector of selected attributes.

Arguments

attributes

a character vector of all attributes to search in

eval.fun

a function taking as first parameter a character vector of all attributes and returning a numeric indicating how important a given subset is

Author

Piotr Romanski

Details

The algorithm starts with a random attribute set. Then it evaluates all its neighbours and chooses the best one. It might be susceptible to local maximum.

See Also

forward.search, backward.search, best.first.search, exhaustive.search

Examples

  library(rpart)
  data(iris)
  
  evaluator <- function(subset) {
    #k-fold cross validation
    k <- 5
    splits <- runif(nrow(iris))
    results = sapply(1:k, function(i) {
      test.idx <- (splits >= (i - 1) / k) & (splits < i / k)
      train.idx <- !test.idx
      test <- iris[test.idx, , drop=FALSE]
      train <- iris[train.idx, , drop=FALSE]
      tree <- rpart(as.simple.formula(subset, "Species"), train)
      error.rate = sum(test$Species != predict(tree, test, type="c")) / nrow(test)
      return(1 - error.rate)
    })
    print(subset)
    print(mean(results))
    return(mean(results))
  }
  
  subset <- hill.climbing.search(names(iris)[-5], evaluator)
  f <- as.simple.formula(subset, "Species")
  print(f)