Greedy search

Share:

Description

The algorithms for searching atrribute subset space.

Usage

1
2

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

These algorithms implement greedy search. At first, the algorithms expand starting node, evaluate its children and choose the best one which becomes a new starting node. This process goes only in one direction. forward.search starts from an empty and backward.search from a full set of attributes.

Value

A character vector of selected attributes.

Author(s)

Piotr Romanski

See Also

best.first.search, hill.climbing.search, exhaustive.search

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
  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 <- forward.search(names(iris)[-5], evaluator)
  f <- as.simple.formula(subset, "Species")
  print(f)