self.train.kernel: Self training for a kernel matrix

In PPInfer: Inferring functionally related proteins using protein interaction networks

Description

This function can be used for classification of semi-supervised data by using the kernel support vector machine.

Usage

self.train.kernel(K, y, type = 'response', C = 1, cache = 40,
                  tol = 0.001, shrinking = TRUE, thrConf = 0.9,
                  maxIts = 10, percFull = 1, verbose = FALSE) 

Arguments

K	kernel matrix
y	lable vector
type	one of response, probabilities ,votes, decision indicating the type of output (default: response)
C	cost of constraints violation for SVM (default: 1)
cache	cache memory in MB for SVM (default: 40)
tol	tolerance of termination criterion for SVM (default: 0.001)
shrinking	option whether to use the shrinking-heuristics for OCSVM (default: TRUE)
thrConf	A number between 0 and 1, indicating the required classification confidence for an unlabelled case to be added to the labelled data set with the label predicted predicted by the classification algorithm (default: 0.9)
maxIts	The maximum number of iterations of the self-training process (default: 10)
percFull	A number between 0 and 1. If the percentage of labelled cases reaches this value the self-training process is stoped (default: 1)
verbose	A boolean indicating the verbosity level of the function (default: FALSE)

Value

prediction from the SVM

Author(s)

Dongmin Jung, Xijin Ge

References

Torgo, L. (2016) Data Mining using R: learning with case studies, second edition, Chapman & Hall/CRC.

Examples

data(litG)
litG <- igraph.from.graphNEL(litG)
sg <- decompose(litG, min.vertices = 50)
sg <- sg[[1]]
K <- net.kernel(sg)
y <- rep(NA, length(V(sg)))
y[1:10] <- 1
y[11:20] <- 0
y <- factor(y)
self.train.kernel(K, y)

PPInfer documentation built on Nov. 8, 2020, 7:52 p.m.