wsvm.predict: Predict new test set using wsvm function and compute error...

In wSVM: Weighted SVM with boosting algorithm for improving accuracy

Description

Predict a weighted svm fit and compute error rate.

Usage

    wsvm.predict(X, Y, new.X, new.Y, model, comp.error.rate = FALSE)

Arguments

X	input variable matrix to generate kernel. Data type must be a matrix format.
Y	output variable vector which will be declared as a matrix in SVM. Data type must be a matrix format.
new.X	test predictors.
new.Y	test response.
model	predicted model including alpha and bias terms. The alpha means estimated coefficients(nrow(X) by 1) and bias means bias term.
comp.error.rate	logical value. If true, calculate error rate.

Details

Predict a weighted svm fit.

Value

A function wsvm.predict generates a list consists of values, g, and error.rate.

predicted.values	fitted value at new.X
g	signs of predicted values
error.rate	misclassification error rate

Author(s)

SungWhan Kim [e-mail]
Soo-Heang Eo [e-mail]

See Also

wsvm, wsvm.boost

Examples

# generate a simulation data set using mixture example(page 17, Friedman et al. 2000)

svm.data <- simul.wsvm(set.seeds = 123)
X <- svm.data$X
Y <- svm.data$Y
new.X <- svm.data$new.X
new.Y <- svm.data$new.Y

# run Weighted K-means clustering SVM with boosting algorithm
model <- wsvm(X, Y, c.n = rep(1/ length(Y),length(Y)))

# predict the model and compute an error rate. 
pred <- wsvm.predict(X,Y, new.X, new.Y, model)

Error.rate(pred$predicted.Y, Y)

# add boost algorithm

boo <- wsvm.boost(X, Y, new.X, new.Y, c.n = rep(1/ length(Y),length(Y)), 
    B = 50, kernel.type = list(type = "rbf", par= 0.5), C = 4, 
    eps = 1e-10, plotting = TRUE)
boo

wSVM documentation built on May 2, 2019, 12:24 p.m.