KODAMA (version 1.5)

knn.kodama: k-Nearest Neighbors Classifier.

Description

k-nearest neighbour classification for a test set from a training set.

Usage

knn.kodama(Xtrain, 
           Ytrain, 
           Xtest,
           Ytest=NULL, 
           k, 
           scaling = c("centering","autoscaling"),
           perm.test=FALSE,
           times=1000)

Arguments

Xtrain

a matrix of training set cases.

Ytrain

a classification vector.

Xtest

a matrix of test set cases.

Ytest

a classification vector.

k

the number of nearest neighbors to consider.

scaling

the scaling method to be used. Choices are "centering" or "autoscaling" (by default = "centering"). A partial string sufficient to uniquely identify the choice is permitted.

perm.test

a classification vector.

times

a classification vector.

Value

The function returns a vector of predicted labels.

Details

The function utilizes the Approximate Nearest Neighbor (ANN) C++ library, which can give the exact nearest neighbours or (as the name suggests) approximate nearest neighbours to within a specified error bound. For more information on the ANN library please visit http://www.cs.umd.edu/~mount/ANN/.

References

Bentley JL (1975) Multidimensional binary search trees used for associative search. Communication ACM 1975;18:309-517.

Arya S, Mount DM Approximate nearest neighbor searching Proc. 4th Ann. ACM-SIAM Symposium on Discrete Algorithms (SODA'93);271-280.

Arya S, Mount DM, Netanyahu NS, Silverman R, Wu AY An optimal algorithm for approximate nearest neighbor searching Journal of the ACM 1998;45:891-923.

Cacciatore S, Luchinat C, Tenori L Knowledge discovery by accuracy maximization. Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA KODAMA: an updated R package for knowledge discovery and data mining. Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. Link

See Also

KODAMA

Examples

# NOT RUN {
 data(iris)
 data=iris[,-5]
 labels=iris[,5]
 ss=sample(150,15)

 z=knn.kodama(data[-ss,], labels[-ss], data[ss,], k=5) 
 table(z$Ypred[,5],labels[ss])
# }