Using a notion of acceptable in uncertain ordinal decision making
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Using importances in group preference aggregation to block strategic manipulation
Aggregation operators
Ordinal decision making with a notion of acceptable: denoted ordinal scales
Data mining, rough sets and granular computing
Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
Geometrical Representation of Quantity Space and Its Application to Robot Motion Description
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
A New Universal Combinatorial Operation Model with Unequal Weights
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
A framework for dynamic multiple-criteria decision making
Decision Support Systems
Information Sciences: an International Journal
Prioritized multi-criteria decision making based on preference relations
Computers and Industrial Engineering
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We introduce the concept of upward reinforcement in aggregation as one in which a collection of high scores can reinforce or corroborate each other to give an even higher score than any of the individual arguments. The concept of downward reinforcement is also introduced as one in which low scores reinforce each other. Our concern is with full reinforcement aggregation operators, those exhibiting both upward and downward reinforcement. It is shown that the t-norm and t-conorm operators are not full reinforcement operators. A class of operators called fixed identity MICA operators are shown to exhibit the property of full reinforcement. We present some families of these operators. We use the fuzzy system modeling technique to provide further examples of these operators