ker.score.classifier.cv {RANKS}

Multiple cross-validation with RANKS for classification

Description

Function to classify labels according to an external cross-validation procedure with a kernel-based score method.

Usage

ker.score.classifier.cv(K, ind.pos, m = 5, p = 100, 
alpha = seq(from = 0.05, to = 0.6, by = 0.05), init.seed = 0, 
opt.fun = compute.F, fun = KNN.score, ...)

Arguments

K

matrix. Kernel matrix or any valid symmetric matrix

ind.pos

indices of the positive examples. They are the row indices of RW corresponding to positive examples.

m

number of folds for each cross-validation

p

number of repeated cross-validations

alpha

vector of the quantiles to be tested

init.seed

initial seed for the random generator (def: 0)

opt.fun

: function. Function implementing the metric to choice the optimal threshold. The F-score (compute.F) is the default. Available functions:

- compute.F: F-score (default)

- compute.acc: accuracy.

Any function having two arguments representing the vector of predicted and true labels can be in principle used.

fun

function. It must be a kernel-based score method (default KNN.score)

...

optional arguments for the function fun

Details

Function to classify labels according to an external cross-validation procedure with a kernel-based score method. The optimal threshold for a given class id found by internal cross-validation. Scores are computed by averaging across (possibly) multiple external cross-validations. The optimal quantile and corresponding threshold are selected by internal cross-validation using the F-score (default) or the accuracy as metric.

Value

A list with 4 components:

labels

vector of the predicted labels (1 represents positive, 0 negative)

av.scores

a vector with the average scores across multiple cross-validations. Elements of the vector av.scores correspond to the rows of RW

opt.alpha

the optimal quantile alpha

opt.thresh

the optimal threshold

a vector of the predicted scores for the test set

See Also

rw.kernel-methods, Kernel functions, ker.score.classifier.holdout

Examples

# Nodel label classification of the DrugBank category Penicillins
# on the Tanimoto chemical structure similarity network (1253 drugs)
# using 5 fold cross-validation repeated 3 times 
# and NN-score with 1-step random walk kernel
library(bionetdata);
data(DD.chem.data);
data(DrugBank.Cat);
labels <- DrugBank.Cat[,"Penicillins"];
ind.pos <- which(labels==1);
K <- rw.kernel(DD.chem.data);
res <- ker.score.classifier.cv(K, ind.pos, m = 5, p = 3, fun = NN.score);