20,513 research outputs found
Data granulation by the principles of uncertainty
Researches in granular modeling produced a variety of mathematical models,
such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets,
which are all suitable to characterize the so-called information granules.
Modeling of the input data uncertainty is recognized as a crucial aspect in
information granulation. Moreover, the uncertainty is a well-studied concept in
many mathematical settings, such as those of probability theory, fuzzy set
theory, and possibility theory. This fact suggests that an appropriate
quantification of the uncertainty expressed by the information granule model
could be used to define an invariant property, to be exploited in practical
situations of information granulation. In this perspective, a procedure of
information granulation is effective if the uncertainty conveyed by the
synthesized information granule is in a monotonically increasing relation with
the uncertainty of the input data. In this paper, we present a data granulation
framework that elaborates over the principles of uncertainty introduced by
Klir. Being the uncertainty a mesoscopic descriptor of systems and data, it is
possible to apply such principles regardless of the input data type and the
specific mathematical setting adopted for the information granules. The
proposed framework is conceived (i) to offer a guideline for the synthesis of
information granules and (ii) to build a groundwork to compare and
quantitatively judge over different data granulation procedures. To provide a
suitable case study, we introduce a new data granulation technique based on the
minimum sum of distances, which is designed to generate type-2 fuzzy sets. We
analyze the procedure by performing different experiments on two distinct data
types: feature vectors and labeled graphs. Results show that the uncertainty of
the input data is suitably conveyed by the generated type-2 fuzzy set models.Comment: 16 pages, 9 figures, 52 reference
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039
71421001,71910107002,71771037,71874023
71871149Sichuan University sksyl201705
2018hhs-5
Group Decision Making Based on a Framework of Granular Computing for Multi-Criteria and Linguistic Contexts
The usage of linguistic information involves computing with words, a methodology assuming
linguistic values as computational elements, in group decision-making environments. In recent times, a new
methodology founded on a framework of granular computing has been employed to manage linguistic
information. An advantage of this methodology is that the distribution and the semantics of the linguistic
values, in place of being initially established, are defined by the optimization of a certain criterion. In this
paper, different from the existing approaches, we present a novel approach build on the basis of a granular
computing framework that is able to cope with group decision-making problems defined in multi-criteria
contexts, that is, those in which different criteria are considered to evaluate the possible alternatives for
solving the problem. In particular, it models group decision-making problems in a more realistic way by
taking into account that each criterion has an importance weight and by considering that each decision maker
has a different importance weight for each criterion. This approach makes operational the linguistic values by
associating them with intervals via the optimization of an optimization criterion composed of two important
aspects that must be taken into account in this kind of decision problems, that is, the consensus at the level
of group of decision makers and the consistency at the level of individual decision makers.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Project DPI2016-77677-P, in part by the
RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (``RobĂłtica aplicada a la mejora de la calidad de vida de los ciudadanos.
Fase IV''; S2018/NMT-4331), funded by the ``Programas de Actividades I+D de la Comunidad de Madrid,'' and co-funded by the
Structural Funds of the EU, and in part by the research grant from the AsociaciĂłn Universitaria Iberoamericana de Postgrado (AUIP)
and ConsejerĂa de EconomĂa y Conocimiento de la Junta de AndalucĂa
Managing Interacting Criteria: Application to Environmental Evaluation Practices
The need for organizations to evaluate their environmental practices has been recently increasing. This fact has led to the development of many approaches to appraise such practices. In this paper, a novel decision model to evaluate company’s environmental practices is proposed to improve traditional evaluation process in different facets. Firstly, different reviewers’ collectives related to the company’s activity are taken into account in the process to increase company internal efficiency and external legitimacy. Secondly, following the standard ISO 14031, two general categories of environmental performance indicators, management and operational, are considered. Thirdly, since the assumption of independence among environmental indicators is rarely verified in environmental context, an aggregation operator to bear in mind the relationship among such indicators in the evaluation results is proposed. Finally, this new model integrates quantitative and qualitative information with different scales using a multi-granular linguistic model that allows to adapt diverse evaluation scales according to appraisers’ knowledge
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