Rough-Set-Based Decision Support

Słowiński, Roman; Greco, Salvatore; Matarazzo, Benedetto

doi:10.1007/978-1-4614-6940-7_19

Cited by 30 publications

(5 citation statements)

References 65 publications

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“…In the case of outranking methods, they have been specifically developed to handle low and null degrees of compensation, which is primarily justified by the pairwise-based comparison of alternatives to derive the decision recommendation [ 126 , 31 ]. As far as the decision rules are concerned, they are inherently non-compensatory due to the nature of their modeling, since it is necessary to reach the conditions of the rules to trigger any recommendation [ 158 ].…”

Section: A Taxonomy Of the Mcda Process Characteristicsmentioning

confidence: 99%

How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

Cinelli

Kadziński

Gonzalez

et al. 2020

Omega

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183

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Decision making is a complex task that involves a multitude of perspectives, constraints, and variables. Multiple Criteria Decision Analysis (MCDA) is a process that has been used for several decades to support decision making. It includes a series of steps that systematically help Decision Maker(s) (DM(s)) and stakeholders in structuring a decision making problem, identifying their preferences, and building a decision recommendation consistent with those preferences. Over the last decades, many studies have demonstrated the conduct of the MCDA process and how to select an MCDA method. Until now, there has not been a review of these studies, nor a proposal of a unified and comprehensive high-level representation of the MCDA process characteristics (i.e., features), which is the goal of this paper. We introduce a review of the research that defines how to conduct the MCDA process, compares MCDA methods, and presents Decision Support Systems (DSSs) to recommend a relevant MCDA method or a subset of methods. We then synthesize this research into a taxonomy of characteristics of the MCDA process, grouped into three main phases, (i) problem formulation, (ii) construction of the decision recommendation, and (iii) qualitative features and technical support. Each of these phases includes a subset of the 10 characteristics that helps the analyst implementing the MCDA process, while also being aware of the implication of these choices at each step. By showing how decision making can be split into manageable and justifiable steps, we reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process. A questioning strategy is also proposed to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies. Additionally, we show how the DSSs for MCDA method recommendation can be grouped into three main clusters. This proposal can enhance a traceable and categorizable development of such systems.

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Section: A Taxonomy Of the Mcda Process Characteristicsmentioning

confidence: 99%

How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

Cinelli

Kadziński

Gonzalez

et al. 2020

Omega

Self Cite

183

View full text Add to dashboard Cite

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“…our set of original data sources). Granularity over multiple universes in rough sets is currently used in decision support and management science (Słowiński et al, 2014;Sun and Ma, 2015). We use it here as a formulized approach to measuring granularity.…”

Section: Validation Of Relationship Between Aggregation and Informatimentioning

confidence: 99%

On the Impact of Granularity in Extracting Knowledge from Bioinformatics Data

West

Ali

2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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With the rapidly increasing amount of various types of biological data currently available to researchers, the focus of the biomedical research community has been shifting from pure data generation towards the development of new methodologies for data analytics. Although many researchers continue to focus on approaches developed for analyzing single types of biological data, recent attempts have been made to utilize the availability of heterogeneous data sets that contain various types of data and try to establish tools for data integration and analysis in many bioinformatics applications. Such attempts are expected to increase significantly in this coming decade. While this can be viewed as a positive step towards advancing big data analytics in bioinformatics, it is critical that these integration methodologies are meticulously studied to ensure high quality of the knowledge extracted from the integrated data. In this work, we employ data integration methods to analyze biological data obtained from protein interaction networks and gene expression data. We conduct a study to show that potential problems can arise from integrating or fusing data obtained at different granularity levels and highlight the importance of developing advanced data fusing techniques to integrate various types of biological data for analytical purposes. Further, we explore the impact of granularity from a more formulized approach and the granularity levels significantly impact the quality of knowledge extracted from the integrated data.

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“…Słowiński [1] refers to states or examples of a decision situation as objects. The knowledge discovery paradigm for multi-attribute and multi-criteria decision making is presented.…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

A Survey of the Application of Rough Sets in Intelligent Decision Support System

Han¹

2015

Proceedings of the 2015 International Conference on Applied Science and Engineering Innovation

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Intelligent decision support system which can help to make decision in solving the complex decision problems through logical reasoning is the combination of decision support system and artificial intelligence technology. Rough sets technology is a mathematical tool for data analysis to deal with fuzzy and uncertainty information. The latest researches of two applications of rough sets in intelligent decision support system which include knowledge acquisition and uncertain reasoning are reviewed.

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Rough-Set-Based Decision Support

Cited by 30 publications

References 65 publications

How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

On the Impact of Granularity in Extracting Knowledge from Bioinformatics Data

A Survey of the Application of Rough Sets in Intelligent Decision Support System

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