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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 ...
Rough information set and its applications in decision making
(Institute of Electrical and Electronics Engineers Inc., 2017)
The decision making in the real world is inevitably characterized with vagueness, and imprecision due to incomplete knowledge. To this end, we combine the information set with the rough set theory to represent both the ...
Linguistic discriminative aggregation in multicriteria decision making
(John Wiley and Sons Ltd, 2016)
Several new aggregation operators are proposed in the context of multicriteria decision making (MCDM) in the linguistic domain. The proposed operators first infer the discrimination index, based on the extent of variability ...
Learning attitudinal decision model through pair-wise preferences
(Emerald Publishing Limited, 2018)
This paper aims to learn a decision-maker’s (DM’s) behavioral process that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the ...
Decision aiding model with Entropy-based subjective utility
(Elsevier, 2018-08-27)
An entropy-based method is presented to model a decision-maker’s (DM’s) subjective utility for a criterion value. The proposed method considers distribution of all the values that the criterion takes for the given set of ...
Learning of a decision-maker’s preference zone with an evolutionary approach
(IEEE, 2018-05-30)
A new evolutionary-learning algorithm is proposed to learn a decision maker (DM)’s best solution on a conflicting multiobjective space. Given the exemplary pairwise comparisons of solutions by a DM, we learn an ideal point ...
Modelling subjective utility through entropy
(Taylor & Francis Group, 2018-04-25)
We introduce a novel entropy framework for the computation of utility on the basis of an agent’s subjective evaluation of the granularised information source values. A concept of evaluating agent as an information gain ...