Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making
In all organizations, many decision analysts acquire their skills through the experience of facing challenges to structure complex problems. Therefore, every day, the use of tools to integrate indicators through multi-attribute ordering, component-based separation, and clustering to reduce the crite...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/10/906 |
| _version_ | 1797574706004492288 |
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| author | Martha Ramírez Patricia Melin Oscar Castillo |
| author_facet | Martha Ramírez Patricia Melin Oscar Castillo |
| author_sort | Martha Ramírez |
| collection | DOAJ |
| description | In all organizations, many decision analysts acquire their skills through the experience of facing challenges to structure complex problems. Therefore, every day, the use of tools to integrate indicators through multi-attribute ordering, component-based separation, and clustering to reduce the criteria required for decision-making and the achievement of goals and objectives is more frequent. Thus, our proposal consists of a new hybrid-hierarchical model for the classification and prediction of country indicators such as inflation, unemployment, population growth, and labor force, among others, in a decision-making environment using unsupervised neural networks and type-3 fuzzy systems. The contribution is achieving a type-3 fuzzy aggregation method in which the hierarchy is first represented by neural networks and later a set of type-1, type-2, and type-3 systems to combine the results, which allows multiple indicators to be separated and then integrated in an appropriate fashion. We can point out as one of the advantages of utilizing the method that the user can evaluate a range of qualities in multiple variables through the classification and prediction of time series attributes and assess a range of qualities for decision-making with uncertainty, according to the results of the simulations carried out. |
| first_indexed | 2024-03-10T21:26:08Z |
| format | Article |
| id | doaj.art-17dca9f9d6a5485089206a17b1b820d6 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-10T21:26:08Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-17dca9f9d6a5485089206a17b1b820d62023-11-19T15:37:43ZengMDPI AGAxioms2075-16802023-09-01121090610.3390/axioms12100906Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-MakingMartha Ramírez0Patricia Melin1Oscar Castillo2Tijuana Institute of Technology, TecNM, Calzada Tecnologico, S/N, Tijuana 22379, MexicoTijuana Institute of Technology, TecNM, Calzada Tecnologico, S/N, Tijuana 22379, MexicoTijuana Institute of Technology, TecNM, Calzada Tecnologico, S/N, Tijuana 22379, MexicoIn all organizations, many decision analysts acquire their skills through the experience of facing challenges to structure complex problems. Therefore, every day, the use of tools to integrate indicators through multi-attribute ordering, component-based separation, and clustering to reduce the criteria required for decision-making and the achievement of goals and objectives is more frequent. Thus, our proposal consists of a new hybrid-hierarchical model for the classification and prediction of country indicators such as inflation, unemployment, population growth, and labor force, among others, in a decision-making environment using unsupervised neural networks and type-3 fuzzy systems. The contribution is achieving a type-3 fuzzy aggregation method in which the hierarchy is first represented by neural networks and later a set of type-1, type-2, and type-3 systems to combine the results, which allows multiple indicators to be separated and then integrated in an appropriate fashion. We can point out as one of the advantages of utilizing the method that the user can evaluate a range of qualities in multiple variables through the classification and prediction of time series attributes and assess a range of qualities for decision-making with uncertainty, according to the results of the simulations carried out.https://www.mdpi.com/2075-1680/12/10/906classificationdecision-makinghybrid-hierarchical modelinterval type-3 fuzzy systemneural networksprediction |
| spellingShingle | Martha Ramírez Patricia Melin Oscar Castillo Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making Axioms classification decision-making hybrid-hierarchical model interval type-3 fuzzy system neural networks prediction |
| title | Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making |
| title_full | Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making |
| title_fullStr | Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making |
| title_full_unstemmed | Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making |
| title_short | Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making |
| title_sort | interval type 3 fuzzy aggregation for hybrid hierarchical neural classification and prediction models in decision making |
| topic | classification decision-making hybrid-hierarchical model interval type-3 fuzzy system neural networks prediction |
| url | https://www.mdpi.com/2075-1680/12/10/906 |
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