Intercriteria analysis over normalized data

Atanassov, Krassimir; Atanassova, Vassia; Chountas, Panagiotis; Mitkova, Magdalena; Sotirova, Evdokia; Sotirov, Sotir; Stratiev, Dicho

doi:10.1109/is.2016.7737480

Cited by 9 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The construction of IC matrix K can be used to search relations between the criteria because the method compares homogeneous data relatively to a same column. Atanassov in [14] prescribes 5 the normalization of the element S ij of score matrix S by taking…”

Section: Atanassov's Inter-criteria Analysis (Icra)mentioning

confidence: 99%

“…The construction of the N × N IC matrix K is based on the pairwise comparisons between every two criteria along all evaluated alternatives. More precisely in [14] the degree of agreement between criteria C j and C j ′ µ jj ′ , and their degree of disagreement ν jj ′ are calculated by…”

Section: Atanassov's Inter-criteria Analysis (Icra)mentioning

confidence: 99%

“…By construction the IC matrix K is always a symmetric matrix. Atanassov provides explicit formulas in [14] for K µ jj ′ and K ν jj ′ which depend on a particular choice of the signum function. Because of this the results of K µ jj ′ and K ν jj ′ are disputable and that is why some authors [22], [28] propose other methods to calculate K µ jj ′ and K ν jj ′ values for making the Inter-Criteria Analysis.…”

Section: Atanassov's Inter-criteria Analysis (Icra)mentioning

confidence: 99%

See 2 more Smart Citations

Inter-Criteria Analysis Based on Belief Functions for GPS Surveying Problems

Fidanova¹,

Dezert²,

Tchamova³

2019

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)

View full text Add to dashboard Cite

In this paper we present an application of a new Belief Function-based Inter-Criteria Analysis (BF-ICrA) approach for Global Positioning System (GPS) Surveying Problems (GSP). GPS surveying is an NP-hard problem. For designing Global Positioning System surveying network, a given set of earth points must be observed consecutively. The survey cost is the sum of the distances to go from one point to another one. This kind of problems is hard to be solved with traditional numerical methods. In this paper we use BF-ICrA to analyze an Ant Colony Optimization (ACO) algorithm developed to provide near-optimal solutions for Global Positioning System surveying problem.

show abstract

Section: Atanassov's Inter-criteria Analysis (Icra)mentioning

confidence: 99%

Section: Atanassov's Inter-criteria Analysis (Icra)mentioning

confidence: 99%

Section: Atanassov's Inter-criteria Analysis (Icra)mentioning

confidence: 99%

See 1 more Smart Citation

Inter-Criteria Analysis Based on Belief Functions for GPS Surveying Problems

Fidanova¹,

Dezert²,

Tchamova³

2019

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)

View full text Add to dashboard Cite

show abstract

“…The construction of IC matrix can be used to search relations between the criteria because the method compares homogeneous data relatively to a same column. In [32] Atanassov prescribes to normalize the score matrix before applying ICrA as follows…”

mentioning

confidence: 99%

Simplification of Multi-Criteria Decision-Making Using Inter-Criteria Analysis and Belief Functions

Dezert

Tchamova

Han

et al. 2019

2019 22th International Conference on Information Fusion (FUSION)

View full text Add to dashboard Cite

In this paper we propose a new Belief Functionbased Inter-Criteria Analysis (BF-ICrA) for the assessment of the degree of redundancy of criteria involved in a multicriteria decision making (MCDM) problem. This BF-ICrA method allows to simplify the original MCDM problem by withdrawing all redundant criteria and thus diminish the complexity of MCDM problem. This is of prime importance for solving large MCDM problems whose solution requires the fusion of many belief functions. We provide simple examples to show how this new BF-ICrA works.

show abstract

“…Later, the method was upgraded to work with input data in the form of intuitionistic fuzzy data[9,23] and normalized data[2], and it is expected to work well with linguistic variables with introduced ordering.…”

mentioning

confidence: 99%

Computational complexity and influence of numerical precision on the results of intercriteria analysis in the decision making process

Atanassova¹,

Roeva²

2018

NIFS

Self Cite

View full text Add to dashboard Cite

The present step from the research on InterCriteria Analysis (ICrA) discusses the issues of the computational complexity of the algorithm developed, and the influence which the numerical precision has on the results of its work. These questions are important from both theoretical, and practical point of view, especially in the context of the application of the method in support of the decision making process.

show abstract

Intercriteria analysis over normalized data

Cited by 9 publications

References 2 publications

Inter-Criteria Analysis Based on Belief Functions for GPS Surveying Problems

Inter-Criteria Analysis Based on Belief Functions for GPS Surveying Problems

Simplification of Multi-Criteria Decision-Making Using Inter-Criteria Analysis and Belief Functions

Computational complexity and influence of numerical precision on the results of intercriteria analysis in the decision making process

Contact Info

Product

Resources

About