Decision-Theoretic Rough Set: A Fusion Strategy

Yin, Tao; Mao, Xiaojuan; Zhang, Ying; Ma, Yiting; Ju, Hengrong; Ding, Weiping

doi:10.1109/access.2020.3042799

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…He developed dynamic maintenance of decision rules for the complexity analysis and extensive experiments on UCI data [6]. Fuzzy set theory is receiving more and more attention in practical applications, and these researches involve attribute discretization, interval-valued systems, attribute reduction, information filling, etc [7]. Inspired by such approaches, this paper adopts the fuzzy set theory to solve the sensitivity analysis problem of complicated experiments, tries to transform the input-output relationship into a decision information system, and explores the minimum set of rules by attribute discretization and attribute reduction, so as to form effective criterion and knowledge to guide the experiment optimization.…”

Section: Introductionmentioning

confidence: 99%

A decision fusion-based method for global sensitivity analysis of complicated experiments

Jiaxin,

Weiwei,

Weiwei

et al. 2024

J. Phys.: Conf. Ser.

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In real world, the relationship between experimental input and output has become more and more complex, which brings great challenges to the sensitivity analysis. This paper proposes a decision fusion based global sensitivity analysis method for complicated experiments, which not only provides quantitative evluation of the input factor influence on experimental results, but also mines the correlation and form the explicit criteria in IF-THEN fomation for further guidance. The theory of decision information system and continuous attribute discretization is presented first for transforming the experimental input and output into a decision table. In order to calculate the sensitivity of the factors and extract valid correlation criterions between conditional attributes and decision attributes simultaneously, the discrimination matrx is utilized for attribute reduction. Then a sensitivity analysis method based on decision fusion is proposed by organically assembling experiment design, attribute discretization, the discrimization matrix, and attribute reduction. Finally, the effectiveness and practicality of the proposed method were verified by the application of sensitivity analysis in hypersonic vehicle re-entry trajectory experiment.

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Section: Introductionmentioning

confidence: 99%

A decision fusion-based method for global sensitivity analysis of complicated experiments

Jiaxin,

Weiwei,

Weiwei

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Decision-theoretic rough sets model (DTRS) [3,4] is an important decision-making model based on probability and risk-cost sensitivity for solving practical decision problems with uncertain data, and multigranulation decision theory is devoted to analyse target decision objects from multilevels and multi-angles [5][6][7][8]. DTRS and a large number of extended models have been studied to address the corresponding requirements in recent years [9][10][11][12][13]. However, most proposed analysis methods in the light of the classical rough set theory can only be used to process single type of data, such as symbolic data, and there are some limitations in the processing of complex data such as numerical or hybrid-valued type decision systems [14].…”

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confidence: 99%

Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets

Chen

Wei

et al. 2021

CAAI Trans on Intel Tech

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It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions. The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision-making problems from multi-levels and multi-angles, and the neighbourhood rough set model (NRS) can facilitate the analysis and processing of numerical or mixed type data, and can address the limitation of multigranulation decision-theoretic rough sets (MG-DTRS), which is not easy to cope with complex data. Based on the in-depth study of hybrid-valued decision systems and MG-DTRS models, this study analysed neighbourhood MG-DTRS (NMG-DTRS) deeply by fusing MG-DTRS and NRS; a matrixbased approach for approximation sets of NMG-DTRS model was proposed on the basis of the matrix representations of concepts; the positive, boundary and negative domains were constructed from the matrix perspective, and the concept of positive decision recognition rate was introduced. Furthermore, the authors explored the related properties of NMG-DTRS model, and designed and described the corresponding solving algorithms in detail. Finally, some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm, but also showed the relationship between the decision recognition rate and the granularity and threshold. K E Y W O R D S decision domains, decision making, NMG-DTRS, rough set theoryThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Decision-Theoretic Rough Set: A Fusion Strategy

Cited by 2 publications

References 40 publications

A decision fusion-based method for global sensitivity analysis of complicated experiments

A decision fusion-based method for global sensitivity analysis of complicated experiments

Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets

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