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Representing uncertainty with information sets
(Institute of Electrical and Electronics Engineers Inc., 2016)
We develop new methods for the representation of uncertainty in the granularized information source values by making use of the entropy framework in the possibilistic domain. An information-theoretic entropy function is ...
Probabilistic variable precision fuzzy rough sets
(Institute of Electrical and Electronics Engineers Inc., 2016)
In the real world, we often encounter varying membership grades due to varying information source values. The fuzzy rough set model is refurbished to develop probabilistic variable precision fuzzy rough set (P-VP-FRS) to ...
On learning of choice models with interactive attributes
(IEEE Computer Society, 2016)
Introducing recent advances in the machine learning techniques to state-of-the-art discrete choice models, we develop an approach to infer the unique and complex decision making process of a decision-maker (DM), which is ...
Discriminative aggregation operators for multi criteria decision making
(Elsevier Ltd, 2017)
A general aggregation formalism for multi criteria decision making (MCDM) applications is presented. Using this formalism, we derive the existing aggregation operators, and also develop some new ones. The proposed general ...
Representation of uncertainty with information and probabilistic information granules
(Springer, 2017)
Linguistic representations by human brain are often characterized with an intertwined combination of imprecision (due to incomplete knowledge), vagueness, or uncertainty. A powerful framework of information and probabilistic ...