ConsRank (version 2.0.1)

DECOR: Differential Evolution algorithm for Median Ranking

Description

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

Usage

DECOR(X, Wk = NULL, NP = 15, L = 100, FF = 0.4, CR = 0.9,
  FULL = FALSE)

Arguments

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.

Value

a "list" containing the following components:

Consensus		the Consensus Ranking
Tau		averaged TauX rank correlation coefficient

Details

It works with a very large number of items to be ranked. Empirically, the number of population individuals (the NP parameter) can be set equal to 10, 20 or 30 for problems till 20, 50 and 100 items. Both scaling rate and crossover ratio (parameters FF and CR) must be set by the user. The default options (FF=0.4, CR=0.9) work well for a large variety of data sets

References

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.

See Also

FASTcons FAST algorithm.

QuickCons Quick algorithm.

EMCons Branch-and-bound algorithm.

Examples

# NOT RUN {
data(EMD)
CR=DECOR(EMD[,1:15],EMD[,16])

# }