An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer

Chatterjee, Saikat; Das, Ananya

doi:10.1007/s00500-023-07939-x

Cited by 10 publications

(4 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the application of Correlation (23) does not require the normalization of λ p , p = 1, 2, ..., q like Correlation (22).…”

Section: Third Techniquementioning

confidence: 99%

“…An overall scheme of the system's operation is illustrated in Figure 2. The fundamental difference between the model operationalized in the multi-attribute decision-making system illustrated in Figure 2 and other systems [23,25,27,29] is the possibility of considering quantitative and qualitative information (in different formats) The fundamental difference between the model operationalized in the multi-attribute decision-making system illustrated in Figure 2 and other systems [23,25,27,29] is the possibility of considering quantitative and qualitative information (in different formats) and conflicting objectives (maximization and minimization) for the proper consideration of the uncertainty of objectives.…”

Section: Multi-criteria Decision-making System (Mdms2) Implementationmentioning

confidence: 99%

“…The system offers an important advance over existing systems, for two reasons. Existing systems ignore objective uncertainty [22,23], do not simultaneously deal with quantitative and qualitative information [24,25], and do not offer flexibility during the process evaluation of criteria and alternatives [26,27]. In particular, the process of evaluating criteria and alternatives in existing multi-criteria decision-making systems is carried out through a unique format, mostly through paired comparison [28,29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

Ferreira,

Ribeiro,

Schuffner

et al. 2024

Axioms

View full text Add to dashboard Cite

This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper’s results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the “.NET” framework. The computing system allows one to represent decision-makers’ preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper’s results have a general character and were applied to analyze power engineering problems.

show abstract

“…Note that the application of Correlation (23) does not require the normalization of λ p , p = 1, 2, ..., q like Correlation (22).…”

Section: Third Techniquementioning

confidence: 99%

Section: Multi-criteria Decision-making System (Mdms2) Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

Ferreira,

Ribeiro,

Schuffner

et al. 2024

Axioms

View full text Add to dashboard Cite

show abstract

“…At the same time, a new method for classifier induction should allow its development for the different types of initial data. In particular, we intend to consider and systemize classification signals [29,34], large-dimensional data [35,36], numeric data [37,38] and linguistic data [28,39]. An analysis of these studies shows that there are differences in the pre-processing steps of the initial data.…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine

2023

View full text Add to dashboard Cite

The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. Medicine 4.0, or smart medicine, can be considered as a structural association of such areas as AI-based medicine, telemedicine, and precision medicine. Each of these areas has its own characteristic data, along with the specifics of their processing and analysis. Nevertheless, at present, all these types of data must be processed simultaneously, in order to provide the most complete picture of the health of each individual patient. In this paper, after a brief analysis of the topic of medical data, a new classification method is proposed that allows the processing of the maximum number of data types. The specificity of this method is its use of a fuzzy classifier. The effectiveness of this method is confirmed by an analysis of the results from the classification of various types of data for medical applications and health problems. In this paper, as an illustration of the proposed method, a fuzzy decision tree has been used as the fuzzy classifier. The accuracy of the classification in terms of the proposed method, based on a fuzzy classifier, gives the best performance in comparison with crisp classifiers.

show abstract

Variational Circuit Based Hybrid Quantum-Classical Algorithm VC-HQCA

Lariane,

Belhadef

2024

Information Systems Engineering and Management

View full text Add to dashboard Cite

An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer

Cited by 10 publications

References 68 publications

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine

Variational Circuit Based Hybrid Quantum-Classical Algorithm VC-HQCA

Contact Info

Product

Resources

About