ibelief (version 1.3.1)

decisionDST: Decision Rules

Description

Different rules for making decisions in the framework of belief functions

Usage

decisionDST(mass, criterion, r = 0.5, sDec = 1:nrow(mass),
  D = Dcalculus(nrow(mass)))

Arguments

mass

The matrix containing the masses. Each column represents a piece of mass.

criterion

The decision baseline:

criterion=1 maximum of the plausibility

criterion=2 maximum of the credibility

criterion=3 maximum of the credibility with rejection

criterion=4 maximum of the pignistic probability

criterion=5 Appriou criterion (decision onto \(2^\Theta\))

criterion=6 Distance criterion (decision onto a given subset (sDec) of \(2^\Theta\))

r

The parameter in BayesianMass function. If criterion 5 is used, it should be given. Otherwise it will be set to the default value 0.5.

sDec

The parameter for the set on which we want to decide. It should be a subset of {1,2,3,...,\(2^n\)}, where \(n\) is the number of elements in \(\Theta\). If criterion 6 is used, it should be given; Otherwise it will be set as the default value \(2^\Theta\).

D

The parameter for the used matrix in Jousselme distance. If criterion 6 is used, it should be given. Otherwise it will be set as default Otherwise it will be calculated.

Value

The decision vector. E.g., in classification problem, class labels.

Examples

# NOT RUN {
m1=c(0,0.4, 0.1, 0.2, 0.2, 0, 0, 0.1);
m2=c(0,0.2, 0.3, 0.1, 0.1, 0, 0.2, 0.1);
m3=c(0.1,0.2, 0, 0.1, 0.1, 0.1, 0, 0.3);

m3d=discounting(m3,0.95);

M_comb_Smets=DST(cbind(m1,m2,m3d),1);
M_comb_PCR6=DST(cbind(m1,m2),8);

class_fusion=decisionDST(M_comb_Smets,1)
class_fusion=decisionDST(M_comb_PCR6,1)
class_fusion=decisionDST(M_comb_Smets,5,0.5)
class_fusion=decisionDST(cbind(M_comb_Smets,M_comb_PCR6),1)
sDec<-c(2,3,4)
class_fusion=decisionDST(M_comb_Smets,6, sDec = sDec)

# }