mistral (version 2.1.0)

LSVM: Linear Support Vector Machine under monotonicity constraints

Description

Produce a globally increasing binary classifier built from linear monotonic SVM

Usage

LSVM(x, A.model.lsvm, convexity)

Arguments

x

a set of points where the class must be estimated.

A.model.lsvm

a matrix containing the parameters of all hyperplanes.

convexity

Either -1 if the set of data associated to the label "-1" is convex or +1 otherwise.

Value

An object of class integer representing the class of x

res

A vector of -1 or +1.

Details

LSVM is a monotonic binary classifier built from linear SVM under the constraint that one of the two classes of data is convex.

References

• R.T. Rockafellar: Convex analysis Princeton university press, 2015.

• N. Bousquet, T. Klein and V. Moutoussamy : Approximation of limit state surfaces in monotonic Monte Carlo settings Submitted .

See Also

modelLSVM

Examples

# NOT RUN {
# A limit state function
f <- function(x){  sqrt(sum(x^2)) - sqrt(2)/2 }

# Creation of the data sets
n <- 200
X <- matrix(runif(2*n), nrow = n)
Y <- apply(X, MARGIN = 1, function(w){sign(f(w))})

#The convexity is known
# }
# NOT RUN {
  model.A <- modelLSVM(X, Y, convexity = -1)
  m <- 10
  X.test <- matrix(runif(2*m), nrow = m)
  classOf.X.test <- LSVM(X.test, model.A, convexity = -1)
# }
# NOT RUN {
# }