hill.climbing.search: Hill climbing search

In FSelector: Selecting Attributes

Hill climbing search

Description

The algorithm for searching atrribute subset space.

Usage

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

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

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.

Value

A character vector of selected attributes.

Author(s)

Piotr Romanski

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)

  

FSelector documentation built on Aug. 23, 2023, 1:08 a.m.