Browsing Journal Articles by Author "Aggarwal, Manish"

Now showing items 1-17 of 17

    • Adaptive linguistic weighted aggregation operators for multi-criteria decision making 

      Aggarwal, Manish (Springer, 2017)
      In this paper, we propose new aggregation operators for multi-criteria decision making under linguistic settings. The proposed operators are based on two sets of criteria weights. Besides the primary conventional criteria ...

    • Confidence soft sets and applications in supplier selection 

      Aggarwal, Manish (Sciencedirect, 2019)
      The evaluation of the alternatives against multiple criteria is of the utmost importance in a multi-criteria de-cisionmaking(MCDM)problem.Itisoftenthecasethattheexpertshaveavaryingdegreeofconfidenceintheirevaluations.Tha ...

    • Decision aiding model with Entropy-based subjective utility 

      Aggarwal, Manish (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 ...

    • Discriminative aggregation operators for multi criteria decision making 

      Aggarwal, Manish (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 ...

    • Intuitionistic fuzzy logit model of discrete choice 

      Aggarwal, Manish (IEEE Xplore, 2019)
      In the real-world multicriteria decision making, the evaluations of the various criteria are often vague (or not crisp). The existing choice models are difficult to apply in such situations. In this paper, we introduce an ...

    • Learning attitudinal decision model through pair-wise preferences 

      Aggarwal, Manish (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 ...

    • Learning of a decision-maker’s preference zone with an evolutionary approach 

      Aggarwal, Manish (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 ...

    • Learning of aggregation models in multi criteria decision making 

      Aggarwal, Manish (Elsevier, 2017)
      Generalized attitudinal Choquet integral (GACI) is a recent aggregation operator that subsumes a multi- tude of aggregation operators, including both linear as well as non-linear and exponential integrals. In this study, ...

    • Linguistic discriminative aggregation in multicriteria decision making 

      Aggarwal, Manish (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 ...

    • Modelling subjective utility through entropy 

      Aggarwal, Manish (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 ...

    • A new family of Fuzzy Discrete Choice Models 

      Aggarwal, Manish (IEEE Xplore, 2019)
      Often in real-world decision making, it is difficult to crisply evaluate the utility values as required in the case of conventional choice models. Besides, a decision maker (DM) has his/her own relative importance for each ...

    • On learning of choice models with interactive attributes 

      Aggarwal, Manish (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 ...

    • Preferences-based learning of multinomial logit model 

      Aggarwal, Manish (Springer, 2019)
      We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. ...

    • Probabilistic variable precision fuzzy rough sets 

      Aggarwal, Manish (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 ...

    • Representation of uncertainty with information and probabilistic information granules 

      Aggarwal, Manish (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 ...

    • Representing uncertainty with information sets 

      Aggarwal, Manish; Hanmandlu, M. (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 ...

    • Rough information set and its applications in decision making 

      Aggarwal, Manish (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 ...