Dominance-based rough set approach for groups in multicriteria classification problems

Chakhar, Salem; Saad, Inès

doi:10.1016/j.dss.2012.05.050

Cited by 50 publications

(47 citation statements)

References 25 publications

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“…Normally many researchers and practitioners applied a single decision analysis method in problem-solving to avoid complexity. However, MCDA method has been widely used in solving problems either with uncertain or precise criteria and alternatives [28].…”

Section: Multi-criteria Decision Analysis (Mcda)mentioning

confidence: 99%

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Mohamad

Selamat

Krejcar

et al. 2015

Computational Collective Intelligence

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Abstract. Rough set theory (RST) is one of the data mining tools, which have many capabilities such as to minimize the size of an input data and to produce sets of decision rules from a set of data. RST is also one of the great techniques used in dealing with ambiguity and uncertainty of datasets. It was introduced by Z. Pawlak in 1997 and until now, there are many researchers who really make use of its advantages either to make an enhancement of the RST or to apply in various research areas such as in decision analysis, pattern recognition, machine learning, intelligent systems, inductive reasoning, data preprocessing, knowledge discovery, and expert systems. This paper presents a recent study on the elementary concepts of RST and its implementation in the multi-criteria decision analysis (MCDA) problems.

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Section: Multi-criteria Decision Analysis (Mcda)mentioning

confidence: 99%

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Mohamad

Selamat

Krejcar

et al. 2015

Computational Collective Intelligence

View full text Add to dashboard Cite

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“…The crucial concepts incorporated in the DRSA sorting method proposed by Sun and Ma (2015) are a dominance relation on the set of multiple sorting decisions (each provided by an individual DM) and a multi-agent conflict analysis framework. Furthermore, Chakhar and Saad (2012) and Chakhar et al (2016) illustrated how to combine individual approximations of class unions and derive collective decision rules that permit classification of all alternatives in a way consistent with the judgments of all DMs. These approaches measure the contribution of each expert to the collective assignment in terms of the individual quality of classification.…”

Section: Review Of Multiple Criteria Sorting Group Decision Methodsmentioning

confidence: 99%

Multiple Criteria Assessment of Insulating Materials with a Group Decision Framework Incorporating Outranking Preference Model and Characteristic Class Profiles

et al. 2017

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We present a group decision making framework for evaluating sustainability of the insulating materials. We tested thirteen materials on a model that was applied to retrofit a traditional rural building through roof's insulation. To evaluate the materials from the socio-economic and environmental viewpoints, we combined life cycle costing and assessment with an adaptive comfort evaluation. In this way, the performances of each coating material were measured in terms of an incurred reduction of costs and consumption of resources, maintenance of the cultural and historic significance of buildings, and a guaranteed indoor thermal comfort. The comprehensive assessment of the materials involved their assignment to one of the three preferenceordered sustainability classes. For this purpose, we used a multiple criteria decision analysis approach that accounted for preferences of a few tens of rural buildings' owners. The proposed methodological framework incorporated an outranking-based preference model to compare the insulating materials with the characteristic class profiles while using the weights derived from the revised Simos procedure. The initial sorting recommendation for each material was validated against the outcomes of robustness analysis that combined the preferences of individual stakeholders either at the output or at the input level. The analysis revealed that the most favorable materials in terms of their overall sustainability were glass wool, hemp fibres, kenaf fibres, polystyrene foam, polyurethane, and rock wool. Electronic supplementary materialThe online version of this article (https://doi.org/10.1007/s10726-017-9549-3) contains supplementary material, which is available to authorized users. B Miłosz Kadziński

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“…An extensive review of multicriteria decision analysis based on dominance rough sets is given in Greco et al [253]. Dominance rough sets have also been applied to ordinal attribute reduction [253,262] and multicriteria classification [263][264][265][266]. Recently, several previous studies extended the fuzzy set based on the dominance rough set approach [106,170,258,267,268].…”

Section: Distribution Papers Based On Decision-makingmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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Dominance-based rough set approach for groups in multicriteria classification problems

Cited by 50 publications

References 25 publications

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Multiple Criteria Assessment of Insulating Materials with a Group Decision Framework Incorporating Outranking Preference Model and Characteristic Class Profiles

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

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