Dempster–Shafer Theory in geographic information systems: A survey

Malpica, José A.; Alonso, María C.; Sanz, María A.

doi:10.1016/j.eswa.2005.11.011

Cited by 70 publications

(30 citation statements)

References 23 publications

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“…The application of this theory for cross platform GRN fusion requires a definition of the degree of belief (mass functions) to assign masses, normally referred to as probability value. The DSTE doesn't mandate the method of computing these masses (probabilities) [8], which makes it a more generalized approach than Bayesian theory [9][10][11]. It adds flexibility to GeNi, as belief masses can be calculated using any GRN reconstruction method (Correlation, Probability value or Mutual Information) to compute the gene co-regulation.…”

Section: Cross Platform Grn Fusionmentioning

confidence: 99%

Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

Sehgal

Gondal

Dooley

et al. 2007

Pattern Recognition in Bioinformatics

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Abstract. Microarray gene expression data is used to model differential activity in Gene Regulatory Networks (GRN) to elucidate complex cellular processes, though network modeling is susceptible to errors due to both noisy nature of gene expression data and platform bias. This intuitively provided the motivation for the development of an innovative technique, which effectively integrates GRN using cross-platform data to minimize the two aforementioned effects. This paper presents a GRN integration (GeNi) framework that fuses crossplatform GRN to remove platform and experimental bias using the Dempster Shafer Theory of Evidence. The proposed model estimates gene co-regulation strength by using mutual information and removes spurious co-regulations through data processing inequality. The method automatically adapts to the data distribution using Belief theory, which does not require a preset threshold to accept co-regulated links.1 GeNi is applied to identify common cancer-related regulatory links in ten different datasets generated by different microarray platforms including cDNA and Affymetrix arrays. Experimental results demonstrate that GeNi can be effectively applied for GRN reconstruction and cross-platform gene network fusion for any gene expression data.

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Section: Cross Platform Grn Fusionmentioning

confidence: 99%

Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

Sehgal

Gondal

Dooley

et al. 2007

Pattern Recognition in Bioinformatics

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“…Other formalisms for combining data such as Dempster-Shafer Method have also been used to combine spatial data (Kontoes et al, 1993;Tangestani and Moore, 2002;Comber et al, 2004;Wadsworth and Hall, 2007). Malpica et al (2007) review the use of Dempster-Shafer approaches in GIS. Dempster-Shafer assesses the belief that a hypothesis is "provable" given the evidence (Comber et al, 2004).…”

Section: Multi-criteria Evaluationmentioning

confidence: 99%

Different methods, different wilds: Evaluating alternative mappings of wildness using fuzzy MCE and Dempster-Shafer MCE

Comber

Carver

Fritz

et al. 2010

Computers, Environment and Urban Systems

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Different multi-criteria evaluation (MCE) approaches are applied to a fuzzy wildness mapping problem in Scotland. The result of fuzzy weighted linear combination and fuzzy order weighted averaging approaches are compared with the application of a Dempster-Shafer MCE. We discuss the implications of different approaches in light of decision making associated with suitability in a context where i) suitability (wildness) may not be very well defined ii) the decision makers may not fully understand the informatics aspects associated with applying weights, but iii) require decisions to be accountable and transparent. In such situations we suggest that the outputs of Dempster-Shafer MCE may be more appropriate than a fully fuzzy model of suitability.

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“…The degree of belief of A (Bel(A)) is the sum of all the basic belief assignments given to the proposition P of the set of outcomes where PA and P≠; while the degree of plausibility (Pl(A)) is the sum of all the basic probability assignments of the focal elements P that intersect the focal element A (Malpica et al 2007).…”

Section: Dempster-shafer Theorymentioning

confidence: 99%

A scoping inquiry into the potential contribution of Subjective Probability Theory, Dempster–Shafer Theory and Possibility Theory in accommodating degrees of belief in traveller behaviour research

Hensher

2014

Travel Behaviour and Society

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There is a small but growing interest in traveller behaviour research on investigating ways to identify and quantify degrees of belief (as subjective probabilities or other propositions) associated with behavioural responses, especially in the context of popular travel choice methods such as stated choice experiments, as a way of adding to our understanding of decision making in real-world contexts that are associated with inevitable risk and uncertainty. This paper reviews three major theories that are not well known in the transportation literature that have been developed in psychology and decision sciences to accommodate belief, namely Subjective Probability Theory, Dempster-Shafer Theory and Possibility Theory. We focus on how degrees of belief are measured in these theories. The key elements of each theoretical approach are compared, including their mathematical properties and evidence patterns. Despite their being few applications to date in transportation, the review promotes the relevance of accounting for degrees of belief in travel choice analysis. Keyword: belief, degree of belief, Subjective Probability Theory, Dempster-Shafer Theory, Possibility TheoryAcknowledgment: This study is supported by the Australian Research Council Discovery Program Grant DP110100454 titled "Assessment of the commuter's willingness to pay a congestion charge under alternative pricing regimes and revenue disbursement plans". We also thank two referees and the Becky Loo for comments.

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Dempster–Shafer Theory in geographic information systems: A survey

Cited by 70 publications

References 23 publications

Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

Different methods, different wilds: Evaluating alternative mappings of wildness using fuzzy MCE and Dempster-Shafer MCE

A scoping inquiry into the potential contribution of Subjective Probability Theory, Dempster–Shafer Theory and Possibility Theory in accommodating degrees of belief in traveller behaviour research

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