A fast attribute reduction method for large formal decision contexts

Chen, Jinkun; Mi, Jin‐Xiao; Xie, Bin; Lin, Yaojin

doi:10.1016/j.ijar.2018.12.002

Cited by 38 publications

(13 citation statements)

References 53 publications

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“…One of the most intensively studied research lines by the research community of FCA in the last years, consists on decreasing the number of attributes of a dataset, preserving the information provided by the dataset [1,2,7,8,10,11,[13][14][15][16][17][18]. In [6], authors proved that every reduction of attributes of a formal context induces an equivalent relation whose equivalent classes are join-semilattices.…”

Section: Introductionmentioning

confidence: 99%

Impact of Local Congruences in Attribute Reduction

Aragón

Medina

Ramírez‐Poussa

2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Section: Introductionmentioning

confidence: 99%

Impact of Local Congruences in Attribute Reduction

Aragón

Medina

Ramírez‐Poussa

2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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“…There are proposals that combine FCA with TWD through three-way concept lattices (including duality [53]) and as a bridge between rough set concept analysis and FCA (see the recent [54]). In [55] authors use attribute reduction techniques on formal decision contexts in order to reduce the number of new concepts to be calculated. The same applies to cost-sensitive attribute reduction methods [56] and radius-based ones [57].…”

Section: Conclusion Related and Future Workmentioning

confidence: 99%

A model of three-way decisions for Knowledge Harnessing

Aranda-Corral

Borrego-Díaz

Galán-Páez

2020

International Journal of Approximate Reasoning

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The present work introduces the Knowledge Harnessing, by showing its theoretical foundations as well as a three-way decision model to deal with it. The problem poses how to extract valid information about a specific context from conflicting or uncertain information received by a system (or agent). With this aim, forgetting variable operators are used to both characterize the problem from the logical point of view and provide a theoretical solution as an acceptance-rejection problem. Since the formalization is semantic in nature (it considers the models of the knowledge base that admit the extracted knowledge), general bounds are provided for acceptance-rejection evaluation on boundary region.

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“…The entropy weight method is an objective weighting method based on normalization matrix calculation and is not suitable for discrete data [68,69]. The entropy weight method analyzes the influence of indicator variation on the weight [70,71], while the attribute reduction set method examines the dependence of decision attributes on conditional attributes [72][73][74]. By combining the weights obtained by both methods, this paper has comprehensively considered the importance of each attribute to decision-making and the influence of information quantity within each attribute on decision-making, thus determining the weight of attributes based on two aspects and making up for the shortcomings of the attribute reduction set method in weight determination.…”

Section: Evaluation Stepsmentioning

confidence: 99%

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

Sun

Yuan

et al. 2019

Sustainability

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In order to evaluate the atmospheric environment sustainability in the provinces of Northeast China, this paper has constructed a comprehensive evaluation model based on the rough set and entropy weight methods. This paper first constructs a Pressure-State-Response (PSR) model with a pressure layer, state layer and response layer, as well as an atmospheric environment evaluation system consisting of 17 indicators. Then, this paper obtains the weight of different indicators by using the rough set method and conducts equal-width discrete analysis and clustering analysis by using SPSS software. This paper has found that different discrete methods will end up with different reduction sets and multiple indicators sharing the same weight. Therefore, this paper has further introduced the entropy weight method based on the weight solution determined by rough sets and solved the attribute reduction sets of different layers by using the Rosetta software. Finally, this paper has further proved the rationality of this evaluation model for atmospheric environment sustainability by comparing the results with those of the entropy weight method alone and those of the rough set method alone. The results show that the sustainability level of the atmospheric environment in Northeast China provinces has first improved, and then worsened, with the atmospheric environment sustainability level reaching the highest level of 0.9275 in 2014, while dropping to the lowest level of 0.6027 in 2017. Therefore, future efforts should focus on reducing the pressure layer and expanding the response layer. Based on analysis of the above evaluation results, this paper has further offered recommendations and solutions for the improvement of atmospheric environment sustainability in the three provinces of Northeast China.

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A fast attribute reduction method for large formal decision contexts

Cited by 38 publications

References 53 publications

Impact of Local Congruences in Attribute Reduction

Impact of Local Congruences in Attribute Reduction

A model of three-way decisions for Knowledge Harnessing

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

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