Consensus in multi-expert decision making problems using penalty functions defined over a Cartesian product of lattices

Authors:
H. Bustince;E. Barrenechea;T. Calvo;S. James;G. Beliakov
Affiliations:
Departamento de Automática y Computación, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain;Departamento de Automática y Computación, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain;Departamento de Ciencias de la Computación, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;School of Information Technology, Deakin University, Australia;School of Information Technology, Deakin University, Australia
Venue:
Information Fusion
Year:
2014

Citing 12
Cited 0

Group decision making with a fuzzy linguistic majority

Fuzzy Sets and Systems
Social choice axioms for fuzzy set aggregation

Fuzzy Sets and Systems - Special issue: Aggregation and best choices of imprecise opinions
Entropy, distance measure and similarity measure of fuzzy sets and their relations

Fuzzy Sets and Systems
Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations

Fuzzy Sets and Systems
Some notes on similarity measure and proximity measure

Fuzzy Sets and Systems
Aggregation operators: properties, classes and construction methods

Aggregation operators
Semiautoduality in a restricted family of aggregation operators

Fuzzy Sets and Systems
Relationship between restricted dissimilarity functions, restricted equivalence functions and normal EN-functions: Image thresholding invariant

Pattern Recognition Letters
Learning valued preference structures for solving classification problems

Fuzzy Sets and Systems
Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting

Pattern Recognition
Aggregation functions based on penalties

Fuzzy Sets and Systems
Quantitative weights and aggregation

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we introduce an algorithm to aggregate the preference relations provided by experts in multi-expert decision making problems. Instead of using a single aggregation function for the whole process, we start from a set of aggregation functions and select, by means of consensus done through penalty functions, the most suitable aggregation function in order to aggregate the individual preferences for each of the elements. An advantage of the method that we propose is that it allows us to recover the classical methods, just by using a single aggregation function. We also present a generalization of the concepts of restricted dissimilarity function and distance between sets for the case where we are working with a Cartesian product of lattices and use such concepts to build penalty functions. Finally, we propose an algorithm that allows us to choose the best combination of aggregation functions for a multi-expert decision making problem.