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Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest Regression [PDF]
Molecules, 2021 Uncertainty measures estimate the reliability of a predictive model. Especially in the field of molecular property prediction as part of drug design, model reliability is crucial.
Thomas-Martin Dutschmann, Knut Baumann
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Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory [PDF]
Entropy, 2022 Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to
Ying Lv +5 more
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Negation of Pythagorean Fuzzy Number Based on a New Uncertainty Measure Applied in a Service Supplier Selection System [PDF]
Entropy, 2020 The Pythagorean fuzzy number (PFN) consists of membership and non-membership as an extension of the intuitionistic fuzzy number. PFN has a larger ambiguity, and it has a stronger ability to express uncertainty. In the multi-criteria decision-making (MCDM)
Haiyi Mao, Rui Cai
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An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making [PDF]
Entropy, 2020 Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of basic probability assignment (BPA) in DS theory ...
Miao Qin, Yongchuan Tang, Junhao Wen
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A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement [PDF]
Entropy, 2021 The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty
Yu Zhang +3 more
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Negation of Belief Function Based on the Total Uncertainty Measure [PDF]
Entropy, 2019 The negation of probability provides a new way of looking at information representation. However, the negation of basic probability assignment (BPA) is still an open issue.
Kangyang Xie, Fuyuan Xiao
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Entropy, 2023 Dempster–Shafer evidence theory is widely used to deal with uncertain information by evidence modeling and evidence reasoning. However, if there is a high contradiction between different pieces of evidence, the Dempster combination rule may give a fusion
Yongchuan Tang +4 more
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The Annals of Occupational Hygiene, 2008 The reporting of measurement uncertainty has recently undergone a major harmonization whereby characteristics of a measurement method obtained during establishment and application are combined componentwise. For example, the sometimes-pesky systematic error is included.
David, Bartley, Göran, Lidén
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Axioms, 2023 In this paper, we suggest a brand new extension of the inverse Lomax distribution for fitting engineering time data. The newly developed distribution, termed the transmuted Topp–Leone inverse Lomax (TTLILo) distribution, is characterized by an additional
Salem A. Alyami +3 more
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On Uncertainty Measure Issues in Rough Set Theory
IEEE Access, 2020 Rough set theory is a tool for dealing with uncertainty problems. How to measure the uncertainty of a knowledge is an important issue in the theory. However, the existing uncertainty measures may not accurately reflect the uncertainty degree.
Jianguo Tang +3 more
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