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IIT Jodhpur

The compensative weighted averaging (CWA) operator is generalized to develop a class of powerful generalized compensative weighted averaging (GCWA) operators with a very fine range of compensatory effects. The conventional means are shown to be the special cases of the proposed GCWA operator. A few extensions are investigated by combining GCWA operator with ordered weighted averaging (OWA) and induced OWA (IOWA) operators. An exponential class of aggregation operators such as exponential CWA, exponential OWA and exponential IOWA operators are developed. The usefulness of GCWA operators is shown through several examples and a case-study. © 2015 Elsevier Ltd. All rights reserved.

School of Artificial Intelligence and Data Science (AIDE)

Computers and Industrial Engineering

Generalized compensative weighted averaging aggregation operators

Abstract We present a method to learn the criteria weights in multi-criteria decision making (MCDM) by applying emerging learning-to-rank machine learning techniques. Given the pairwise preferences by a decision maker (DM), we learn the weights that the DM attaches to the multiple criteria, characterizing each alternative. As the training information, our method requires the pairwise preferences of alternatives, as revealed by the DM. Once, the DM's decision model is learnt in terms of the criteria weights, it can be applied to predict his choices for any new set of alternatives. The empirical validation of the proposed approach is done on a collection of 12 standard datasets. The accuracy values are compared with those obtained for the state-of-the-art methods such as ranking-SVM and TOPSIS. © 2015 Elsevier Inc.

Information Sciences

On learning of weights through preferences

We extend the fuzzy rough set as probabilistic fuzzy rough set (PFRS) on a fuzzy probabilistic approximation space. The proposed PFRS models both vague and uncertain information at the same time. The viability of PFRS in decision making is illustrated through a case study. PFRS is shown to be useful for both probabilistic as well as deterministic problems. © 2015 - IOS Press and the authors. All rights reserved.

Journal of Intelligent and Fuzzy Systems

Probabilistic fuzzy rough sets

Combining established modelling techniques from multiple-criteria decision aiding with recent algorithmic advances in the emerging field of preference learning, we propose a new method that can be seen as an adaptive version of TOPSIS, the technique for order preference by similarity to ideal solution decision model (or at least a simplified variant of this model). On the basis of exemplary preference information in the form of pairwise comparisons between alternatives, our method seeks to induce an 'ideal solution' that, in conjunction with a weight factor for each criterion, represents the preferences of the decision maker. To this end, we resort to probabilistic models of discrete choice and make use of maximum likelihood inference. First experimental results on suitable preference data suggest that our approach is not only intuitively appealing and interesting from an interpretation point of view but also competitive to state-of-the-art preference learning methods in terms of prediction accuracy. © 2014 John Wiley & Sons, Ltd.

World University root node

Journal of Multi-Criteria Decision Analysis

Preference-based Learning of Ideal Solutions in TOPSIS-like Decision Models

Information sets expand the scope of the existing uncertainty theories by representing uncertainty in the information sources. An information set takes into account both the information source values as well as the membership grades. In this study, we investigate in detail the properties of the information sets. The information measures for the information sets are also developed to measure the information content in an information set. © 2014 IEEE.

IEEE International Conference on Fuzzy Systems

The properties and information measures for information sets

In this paper, a new operator for aggregation of uncertain information under intuitionistic fuzzy environment is proposed. A novel approach is proposed for the selection of best alternative action in the face of the imprecise probabilities and the complex attitudinal character of the decision makers (DMs). This approach is distinguished with its capacity to accommodate the linguistic specification of probabilities as provided by human experts directly without the need to determine the fuzzy membership grades. The focus is to compute the net payoff for each alternative in the face of uncertain states of nature and DM's attitude. The proposed operator and the approach are illustrated through two real case studies. © 2013 Wiley Periodicals, Inc.

International Journal of Intelligent Systems

A probabilistic and decision attitude aggregation operator for intuitionistic fuzzy environment

The concept of intuitionistic fuzzy soft set (IFSS) arising from intuitionistic fuzzy set (IFS) is generalized by including a parameter reflecting a moderator's opinion about the validity of the information provided. The resulting generalized intuitionistic fuzzy soft set (GIFSS) finds a special role in the decision making applications. It can evaluate the given criteria along with the moderator's assessment of the furnished data. The properties of GIFSS are investigated and the associated relations called generalized intuitionistic fuzzy soft relations (GIFSR) are given. A similarity measure is given to compare two GIFSSs. As this is not applicable to fuzzy numbers, a new score function is devised to compare two intuitionistic fuzzy numbers (IFNs), the components of IFS. The effectiveness of the proposed GIFSS in decision making is demonstrated on four case studies. © 2013 Elsevier B.V. All rights reserved.