scholarly journals Distance Metrics of D Numbers

2020 ◽  
Author(s):  
Liguo Fei ◽  
Yuqiang Feng
Keyword(s):  
Number Theory ◽  
Incomplete Information ◽  
Distance Function ◽  
Distance Measure ◽  
Complete Information ◽  
Belief Function ◽  
Distance Metrics ◽  
Modeling Methods ◽  
The Difference ◽  
D Numbers

Belief function has always played an indispensable role in modeling cognitive uncertainty. As an inherited version, the theory of D numbers has been proposed and developed in a more efficient and robust way. Within the framework of D number theory, two more generalized properties are extended: (1) the elements in the frame of discernment (FOD) of D numbers do not required to be mutually exclusive strictly; (2) the completeness constraint is released. The investigation shows that the distance function is very significant in measuring the difference between two D numbers, especially in information fusion and decision. Modeling methods of uncertainty that incorporate D numbers have become increasingly popular, however, very few approaches have tackled the challenges of distance metrics. In this study, the distance measure of two D numbers is presented in cases, including complete information, incomplete information, and non-exclusive elements

scholarly journals A Dynamic Distance Measure of Picture Fuzzy Sets and Its Application

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 436
Author(s):  
Ruirui Zhao ◽  
Minxia Luo ◽  
Shenggang Li
Keyword(s):  
Fuzzy Sets ◽  
Distance Measure ◽  
Distance Measures ◽  
Numerical Comparison ◽  
Mathematical Tool ◽  
Practical Applications ◽  
Fuzzy Point ◽  
Point Operator ◽  
The Difference ◽  
Picture Fuzzy Sets

Picture fuzzy sets, which are the extension of intuitionistic fuzzy sets, can deal with inconsistent information better in practical applications. A distance measure is an important mathematical tool to calculate the difference degree between picture fuzzy sets. Although some distance measures of picture fuzzy sets have been constructed, there are some unreasonable and counterintuitive cases. The main reason is that the existing distance measures do not or seldom consider the refusal degree of picture fuzzy sets. In order to solve these unreasonable and counterintuitive cases, in this paper, we propose a dynamic distance measure of picture fuzzy sets based on a picture fuzzy point operator. Through a numerical comparison and multi-criteria decision-making problems, we show that the proposed distance measure is reasonable and effective.

Extended two-dimensional belief function based on divergence measurement

2020 ◽  
pp. 1-8
Author(s):  
Jianping Fan ◽  
Jing Wang ◽  
Meiqin Wu
Keyword(s):  
Belief Function ◽  
Distribution Functions ◽  
Divergence Measure ◽  
Uncertain Information ◽  
Belief Functions ◽  
Two Dimensional ◽  
Probability Distribution Functions ◽  
Basic Probability ◽  
The Difference ◽  
Supporting Degree

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.

scholarly journals Spillovers and R&D Incentive under Incomplete Information

2018 ◽  
Vol 6 (1-2) ◽  
pp. 50-65 ◽  
Author(s):  
Rittwik Chatterjee ◽  
Srobonti Chattopadhyay ◽  
Tarun Kabiraj
Keyword(s):  
Distribution Function ◽  
Incomplete Information ◽  
Private Information ◽  
Complete Information ◽  
Jel Classification ◽  
General Distribution ◽  
Two Stage ◽  
Stage Game ◽  
Alternative Scenarios

Spillovers of R&D outcome affect the R&D decision of a firm. The present paper discusses the R&D incentives of a firm when the extent of R&D spillover is private information to each firm. We construct a two-stage game involving two firms when the firms first decide simultaneously whether to invest in R&D or not, then they compete in quantity. Assuming general distribution function of firm types we compare R&D incentives of firms under alternative scenarios based on different informational structures. The paper shows that while R&D spillovers reduce R&D incentives under complete information unambiguously, however, it can be larger under incomplete information. JEL Classification: D43, D82, L13, O31

scholarly journals A New Burrows Wheeler Transform Markov Distance

2020 ◽  
Vol 34 (04) ◽  
pp. 5444-5453
Author(s):  
Edward Raff ◽  
Charles Nicholas ◽  
Mark McLean
Keyword(s):  
Dna Sequence ◽  
Distance Measure ◽  
Feature Vector ◽  
Distance Metrics ◽  
Prior Work ◽  
Compression Algorithms ◽  
Fixed Length ◽  
Malware Classification ◽  
Classification Tasks ◽  
Burrows Wheeler Transform

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classification tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a fixed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.

scholarly journals Graph diffusion distance: Properties and efficient computation

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249624
Author(s):  
C. B. Scott ◽  
Eric Mjolsness
Keyword(s):  
Distance Measure ◽  
Fixed Time ◽  
Graph Laplacian ◽  
Upper Bounds ◽  
Distance Measures ◽  
Distance Metrics ◽  
Graph Products ◽  
Efficient Computation ◽  
New Family ◽  
Sparsity Constraints

We define a new family of similarity and distance measures on graphs, and explore their theoretical properties in comparison to conventional distance metrics. These measures are defined by the solution(s) to an optimization problem which attempts find a map minimizing the discrepancy between two graph Laplacian exponential matrices, under norm-preserving and sparsity constraints. Variants of the distance metric are introduced to consider such optimized maps under sparsity constraints as well as fixed time-scaling between the two Laplacians. The objective function of this optimization is multimodal and has discontinuous slope, and is hence difficult for univariate optimizers to solve. We demonstrate a novel procedure for efficiently calculating these optima for two of our distance measure variants. We present numerical experiments demonstrating that (a) upper bounds of our distance metrics can be used to distinguish between lineages of related graphs; (b) our procedure is faster at finding the required optima, by as much as a factor of 103; and (c) the upper bounds satisfy the triangle inequality exactly under some assumptions and approximately under others. We also derive an upper bound for the distance between two graph products, in terms of the distance between the two pairs of factors. Additionally, we present several possible applications, including the construction of infinite “graph limits” by means of Cauchy sequences of graphs related to one another by our distance measure.

Incomplete Information in Multidimensional Databases

2003 ◽  
pp. 282-309 ◽  
Author(s):  
Cirtis E. Dyreson ◽  
Torben Bach Pedersen ◽  
Christian S. Jensen
Keyword(s):  
Incomplete Information ◽  
Complete Information ◽  
Multidimensional Data ◽  
Uncertain Information ◽  
Real World Data ◽  
Multidimensional Database ◽  
Simple Strategy ◽  
Multidimensional Databases ◽  
Derived Data ◽  
Base Data

While incomplete information is endemic to real-world data, current multidimensional data models are not engineered to manage incomplete information in base data, derived data, and dimensions. This chapter presents several strategies for managing incomplete information in multidimensional databases. Which strategy to use is dependent on the kind of incomplete information present, and also on where it occurs in the multidimensional database. A relatively simple strategy is to replace incomplete information with appropriate, complete information. The advantage of this strategy is that all multidimensional databases can manage complete information. Other strategies require more substantial changes to the multidimensional database. One strategy is to reflect the incompleteness in computed aggregates, which is possible only if the multidimensional database allows incomplete values in its hierarchies. Another strategy is to measure the amount of incompleteness in aggregated values by tallying how much uncertain information went into their production.

4. The plane and other spaces

2019 ◽  
pp. 64-89
Author(s):  
Richard Earl
Keyword(s):  
Continuous Function ◽  
Distance Function ◽  
Metric Spaces ◽  
Topological Invariants ◽  
The Difference ◽  
Speed Function

Most functions have several numerical inputs and produce more than one numerical output. But even generally continuity requires that we can constrain the difference in outputs by suitably constraining the difference in inputs. ‘The plane and other spaces’ asks more general questions such as ‘is the distance a car has travelled a continuous function of its speed?’ This is a subtle question as neither the input nor output are numbers, but rather functions of time, with input the speed function s(t) and output the distance function d(t). In answering the question, it considers continuity between metric spaces, equivalent metrics, open sets, convergence, and compactness and connectedness, the last two being topological invariants that can be used to differentiate between spaces.

AMERICAN OPTIONS AND INCOMPLETE INFORMATION

2019 ◽  
Vol 22 (06) ◽  
pp. 1950035 ◽  
Author(s):  
ERIK EKSTRÖM ◽  
MARTIN VANNESTÅL
Keyword(s):  
Incomplete Information ◽  
American Options ◽  
Lower Boundary ◽  
Complete Information ◽  
Call Option ◽  
Standard Case ◽  
Underlying Process ◽  
Parameter Values ◽  
American Put

We study the optimal exercise of American options under incomplete information about the drift of the underlying process, and we show that quite unexpected phenomena may occur. In fact, certain parameter values give rise to stopping regions very different from the standard case of complete information. For example, we show that for the American put (call) option it is sometimes optimal to exercise the option when the underlying process reaches an upper (lower) boundary.

scholarly journals A Consistency Check Method for Trusted Hesitant Fuzzy Sets with Confidence Levels Based on a Distance Measure

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Huimin Xiao ◽  
Meiqi Wang ◽  
Xiaoning Xi
Keyword(s):  
Fuzzy Sets ◽  
Distance Measure ◽  
Consistency Check ◽  
Hesitant Fuzzy Sets ◽  
Numerical Examples ◽  
Confidence Levels ◽  
Check Method ◽  
The Difference

This paper proposes a consistency check method for hesitant fuzzy sets with confidence levels by employing a distance measure. Firstly, we analyze the difference between each fuzzy element and its corresponding attribute comprehensive decision value and then obtain a comprehensive distance measure for each attribute. Subsequently, by taking the relative credibility as the weight, we assess the consistency of hesitant fuzzy sets. Finally, numerical examples are put forward to verify the effectiveness and reliability of the proposed method.

scholarly journals A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 142 ◽  
Author(s):  
Qianli Zhou ◽  
Hongming Mo ◽  
Yong Deng
Keyword(s):  
Fuzzy Sets ◽  
Medical Diagnosis ◽  
Distance Measure ◽  
Belief Function ◽  
Intuitionistic Fuzzy Sets ◽  
Divergence Measure ◽  
Pythagorean Fuzzy Sets ◽  
Practical Applications ◽  
Intuitionistic Fuzzy ◽  
Jensen Shannon Divergence

As the extension of the fuzzy sets (FSs) theory, the intuitionistic fuzzy sets (IFSs) play an important role in handling the uncertainty under the uncertain environments. The Pythagoreanfuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic fuzzy sets because of its larger range of describing the membership grades. How to measure the distance of Pythagorean fuzzy sets is still an open issue. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. In order to efficiently deal with uncertainty in practical applications, this paper proposes a new divergence measure of Pythagorean fuzzy sets, which is based on the belief function in Dempster–Shafer evidence theory, and is called PFSDM distance. It describes the Pythagorean fuzzy sets in the form of basic probability assignments (BPAs) and calculates the divergence of BPAs to get the divergence of PFSs, which is the step in establishing a link between the PFSs and BPAs. Since the proposed method combines the characters of belief function and divergence, it has a more powerful resolution than other existing methods. Additionally, an improved algorithm using PFSDM distance is proposed in medical diagnosis, which can avoid producing counter-intuitive results especially when a data conflict exists. The proposed method and the magnified algorithm are both demonstrated to be rational and practical in applications.

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