KODAMA (version 0.0.1)

core: Maximization of Cross-Validateed Accuracy Methods

Description

This function performs the maximization of cross-validated accuracy by an iterative process

Usage

core(x, clbest, Tcycle=20, FUN=KNN.CV, f.par=list(kn=10),  constrain=NULL, fix=NULL, shake=FALSE)

Arguments

x

a matrix.

clbest

a vector to optimize.

Tcycle

number of interative cycle that leads to the maximization of cross-validated accuracy.

FUN

classifier to be consider. Choices are "KNN.CV", "PLS.SVM.CV" , and "PCA.CA.KNN.CV".

f.par

parameters of the classifier.

constrain

a vector of nrow(data) elements. Supervised constraints can be imposed by linking some samples in such a way that if one of them is changed the linked ones must change in the same way (i.e., they are forced to belong to the same class) during the maximization of the cross-validation accuracy procedure. Sample with the same identificative constrain will be forced to stay together.

fix

a vector of nrow(data) elements. The values of this vector must to be TRUE or FALSE. By default all elements are FALSE. Samples with the TRUE fix value will not change the class label defined in W during the maximization of the cross-validation accuracy procedure.

shake

if shake = FALSE the cross-validated accuracy is computed with the class defined in W else the it is not, before the maximization of the cross-validation accuracy procedure.

Value

The function returns a list with 3 items:

c

a classification vector with a maximized cross-validated accuracy.

a

the maximum cross-validated accuracy achieved.

v

a vector of all cross-validated accuracy obtained.

References

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

See Also

KODAMA

Examples

# data(iris)
# u=iris[,-5]
# s=sample(1:150,150,TRUE)
# results=core(u,s)
# unique(s)
# unique(results$c)