[HTML][HTML] Representing uncertainty and imprecision in machine learning: A survey on belief functions
Z Liu, S Letchmunan - Journal of King Saud University-Computer and …, 2024 - Elsevier
Uncertainty and imprecision accompany the world we live in and occur in almost every
event. How to better interpret and manage uncertainty and imprecision play a vital role in …
event. How to better interpret and manage uncertainty and imprecision play a vital role in …
Uncertainty measure in evidence theory with its applications
X Wang, Y Song - Applied Intelligence, 2018 - Springer
Uncertainty measure in evidence theory supplies a new criterion to assess the quality and
quantity of knowledge conveyed by belief structures. As generalizations of uncertainty …
quantity of knowledge conveyed by belief structures. As generalizations of uncertainty …
Plausibility entropy: A new total uncertainty measure in evidence theory based on plausibility function
Y Cui, X Deng - IEEE Transactions on Systems, Man, and …, 2023 - ieeexplore.ieee.org
Evidence theory provides an effective representation and handling framework for uncertain
information. However, the quantification for the uncertainty of mass function in this theory is …
information. However, the quantification for the uncertainty of mass function in this theory is …
A new distance-based total uncertainty measure in the theory of belief functions
Y Yang, D Han - Knowledge-Based Systems, 2016 - Elsevier
The theory of belief functions is a very important and effective tool for uncertainty modeling
and reasoning, where measures of uncertainty are very crucial for evaluating the degree of …
and reasoning, where measures of uncertainty are very crucial for evaluating the degree of …
Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring
Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring
have been presented. In these studies, different ensemble schemes for complex classifiers …
have been presented. In these studies, different ensemble schemes for complex classifiers …
[HTML][HTML] A new definition of entropy of belief functions in the Dempster–Shafer theory
R Jiroušek, PP Shenoy - International Journal of Approximate Reasoning, 2018 - Elsevier
We propose a new definition of entropy of basic probability assignments (BPAs) in the
Dempster–Shafer (DS) theory of belief functions, which is interpreted as a measure of total …
Dempster–Shafer (DS) theory of belief functions, which is interpreted as a measure of total …
Credal-C4. 5: Decision tree based on imprecise probabilities to classify noisy data
In the area of classification, C4. 5 is a known algorithm widely used to design decision trees.
In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A …
In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A …
The negation of a basic probability assignment
In the field of information science, how to represent the uncertain information is still an open
issue. The negation is an important way to represent the information. However, existing …
issue. The negation is an important way to represent the information. However, existing …
On the negation of a Dempster–Shafer belief structure based on maximum uncertainty allocation
X Deng, W Jiang - Information Sciences, 2020 - Elsevier
Abstract Probability theory and Dempster–Shafer theory are two germane theories to
represent and handle uncertain information. Recent study suggested a transformation to …
represent and handle uncertain information. Recent study suggested a transformation to …