knn.cv: k-Nearest Neighbour Classification Cross-Validation

In FNN: Fast Nearest Neighbor Search Algorithms and Applications

k-Nearest Neighbour Classification Cross-Validation

Description

k-nearest neighbour classification cross-validation from training set.

Usage

knn.cv(train, cl, k = 1, prob = FALSE, algorithm=c("kd_tree",
       "cover_tree", "brute"))

Arguments

train	matrix or data frame of training set cases.
cl	factor of true classifications of training set
k	number of neighbours considered.
prob	if this is true, the proportion of the votes for the winning class are returned as attribute prob.
algorithm	nearest neighbor search algorithm.

Details

This uses leave-one-out cross validation. For each row of the training set train, the k nearest (in Euclidean distance) other training set vectors are found, and the classification is decided by majority vote, with ties broken at random. If there are ties for the kth nearest vector, all candidates are included in the vote.

Value

factor of classifications of training set. doubt will be returned as NA. distances and indice of k nearest neighbors are also returned as attributes.

Author(s)

Shengqiao Li. To report any bugs or suggestions please email: [e-mail]

References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

knn and knn.cv in class.

Examples

  data(iris3)
  train <- rbind(iris3[,,1], iris3[,,2], iris3[,,3])
  cl <- factor(c(rep("s",50), rep("c",50), rep("v",50)))
  knn.cv(train, cl, k = 3, prob = TRUE)
  attributes(.Last.value)

FNN documentation built on Sept. 22, 2024, 9:06 a.m.