DECOR {ConsRank} | R Documentation |
Differential evolution algorithm for median ranking detection. It works with full, tied and partial rankings. The solution con be constrained to be a full ranking or a tied ranking
DECOR(X, Wk = NULL, NP = 15, L = 100, FF = 0.4, CR = 0.9, FULL = FALSE)
X |
A N by M data matrix, in which there are N judges and M objects to be judged. Each row is a ranking of the objects which are represented by the columns. Alternatively X can contain the rankings observed only once. In this case the argument Wk must be used |
Wk |
Optional: the frequency of each ranking in the data |
NP |
The number of population individuals |
L |
Generations limit: maximum number of consecutive generations without improvement |
FF |
The scaling rate for mutation. Must be in [0,1] |
CR |
The crossover range. Must be in [0,1] |
FULL |
Default FULL=FALSE. If FULL=TRUE, the searching is limited to the space of full rankings. |
This function is deprecated and it will be removed in the next release of the package. Use function 'consrank' instead.
a "list" containing the following components:
Consensus | the Consensus Ranking | |
Tau | averaged TauX rank correlation coefficient | |
Eltime | Elapsed time in seconds |
Antonio D'Ambrosio antdambr@unina.it and Giulio Mazzeo giuliomazzeo@gmail.com
D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017). A differential evolution algorithm for finding the median ranking under the Kemeny axiomatic approach. Computers and Operations Research, vol. 82, pp. 126-138.
#not run
#data(EMD)
#CR=DECOR(EMD[,1:15],EMD[,16])