Uncertainty learning of rough set-based prediction under a holistic framework

Chen, Degang; Zhang, Xiaoxia; Wang, Xizhao; Liu, Yanjun

doi:10.1016/j.ins.2018.06.033

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…(11) The proofs are similar to(8) and(9).Based on the Definition 10, the rough regions with regard tom i=1 A λ≤ i,k at information level ϕ are as follows: POS λ,ϕ…”

mentioning

confidence: 91%

“…Rough sets (RS) theory is an effective mathematical tool proposed by Pawlak in 1982 to address uncertainties and imprecisions [1]. It has been successfully applied in feature selection [2], safety monitoring data classification [3], decision making [4][5][6], information fusion [7], uncertainty analysis [8,9], medical diagnosis [10], and other fields [11][12][13][14]. Classical RS require strict inclusion relations between their equivalent classes and sets, and because there is no fault-tolerant mechanism, they are limited.…”

Section: Introductionmentioning

confidence: 99%

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Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Xue

Zhang

et al. 2020

Symmetry

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Since the rough sets theory based on the double quantification method was proposed, it has attracted wide attention in decision-making. This paper studies the decision-making approach in Incomplete Ordered Information System (IOIS). Firstly, to better extract the effective information in IOIS, combined with the advantages of set-pair dominance relation and generalized multi-granulation, the generalized multi-granulation set-pair dominance variable precision rough sets (GM-SPD-VPRS) and the generalized multi-granulation set-pair dominance graded rough sets (GM-SPD-GRS) are proposed. Moreover, we discuss their related properties. Secondly, considering the GM-SPD-VPRS and the GM-SPD-GRS describe information from relative view and absolute view, respectively, we further combine the two rough sets to obtain six double-quantitative generalized multi-granulation set-pair dominance rough sets (GM-SPD-RS) models. Among them, the first two models fuse the approximation operators of two rough sets, and investigate the extreme cases of optimistic and pessimistic. The last four models combine the two rough sets by the logical disjunction operator and the logical conjunction operator. Then, we discuss relevant properties and derive the corresponding decision rules. According to the decision rules, an associated algorithm is constructed for one of the models to calculate the rough regions. Finally, we validate the effectiveness of these models with a medical example. The results indicate that the model is effective for dealing with practical problems.

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“…(11) The proofs are similar to(8) and(9).Based on the Definition 10, the rough regions with regard tom i=1 A λ≤ i,k at information level ϕ are as follows: POS λ,ϕ…”

mentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Xue

Zhang

et al. 2020

Symmetry

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show abstract

“…How to measure uncertainty has become an open issue. Some theoretical tools have been proposed, including probability theory [1], fuzzy set theory [2,3], D-numbers [4,5], Z-numbers [6,7], rough set theory [8,9], Dempster-Shafer (D-S) evidence theory [10,11], fractal theory [12,13], etc. D-S evidence theory is one of the most effective tools among them.…”

Section: Introductionmentioning

confidence: 99%

Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance

Hou

Deng

et al. 2021

Energies

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Conflicting evidence affects the final target recognition results. Thus, managing conflicting evidence efficiently can help to improve the belief degree of the true target. In current research, the existing approaches based on belief entropy use belief entropy itself to measure evidence conflict. However, it is not convincing to characterize the evidence conflict only through belief entropy itself. To solve this problem, we comprehensively consider the influences of the belief entropy itself and mutual belief entropy on conflict measurement, and propose a novel approach based on an improved belief entropy and entropy distance. The improved belief entropy based on pignistic probability transformation function is named pignistic probability transformation (PPT) entropy that measures the conflict between evidences from the perspective of self-belief entropy. Compared with the state-of-the-art belief entropy, it can measure the uncertainty of evidence more accurately, and make full use of the intersection information of evidence to estimate the degree of evidence conflict more reasonably. Entropy distance is a new distance measurement method and is used to measure the conflict between evidences from the perspective of mutual belief entropy. Two measures are mutually complementary in a sense. The results of numerical examples and target recognition applications demonstrate that our proposed approach has a faster convergence speed, and a higher belief degree of the true target compared with the existing methods.

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Local rough set-based feature selection for label distribution learning with incomplete labels

Qian

Dong

Wang

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Uncertainty learning of rough set-based prediction under a holistic framework

Cited by 10 publications

References 34 publications

Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance

Local rough set-based feature selection for label distribution learning with incomplete labels

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