A novel fuzzy knowledge graph pairs approach in decision making
Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new reco...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Long, Cu Kim [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 18 vom: 29. Apr., Seite 26505-26534 |
|---|---|
| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:18 ; day:29 ; month:04 ; pages:26505-26534 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-022-13067-9 |
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OLC207906052X |
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| 520 | |a Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems | ||
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10.1007/s11042-022-13067-9 doi (DE-627)OLC207906052X (DE-He213)s11042-022-13067-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Long, Cu Kim verfasserin aut A novel fuzzy knowledge graph pairs approach in decision making 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems M-CFIS-FKG Knowledge graph Fuzzy knowledge graph FKG-pairs Approximate reasoning Decision making Van Hai, Pham aut Tuan, Tran Manh (orcid)0000-0002-1117-7253 aut Lan, Luong Thi Hong aut Chuan, Pham Minh aut Son, Le Hoang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 18 vom: 29. Apr., Seite 26505-26534 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:18 day:29 month:04 pages:26505-26534 https://doi.org/10.1007/s11042-022-13067-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 18 29 04 26505-26534 |
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10.1007/s11042-022-13067-9 doi (DE-627)OLC207906052X (DE-He213)s11042-022-13067-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Long, Cu Kim verfasserin aut A novel fuzzy knowledge graph pairs approach in decision making 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems M-CFIS-FKG Knowledge graph Fuzzy knowledge graph FKG-pairs Approximate reasoning Decision making Van Hai, Pham aut Tuan, Tran Manh (orcid)0000-0002-1117-7253 aut Lan, Luong Thi Hong aut Chuan, Pham Minh aut Son, Le Hoang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 18 vom: 29. Apr., Seite 26505-26534 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:18 day:29 month:04 pages:26505-26534 https://doi.org/10.1007/s11042-022-13067-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 18 29 04 26505-26534 |
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10.1007/s11042-022-13067-9 doi (DE-627)OLC207906052X (DE-He213)s11042-022-13067-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Long, Cu Kim verfasserin aut A novel fuzzy knowledge graph pairs approach in decision making 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems M-CFIS-FKG Knowledge graph Fuzzy knowledge graph FKG-pairs Approximate reasoning Decision making Van Hai, Pham aut Tuan, Tran Manh (orcid)0000-0002-1117-7253 aut Lan, Luong Thi Hong aut Chuan, Pham Minh aut Son, Le Hoang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 18 vom: 29. Apr., Seite 26505-26534 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:18 day:29 month:04 pages:26505-26534 https://doi.org/10.1007/s11042-022-13067-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 18 29 04 26505-26534 |
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10.1007/s11042-022-13067-9 doi (DE-627)OLC207906052X (DE-He213)s11042-022-13067-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Long, Cu Kim verfasserin aut A novel fuzzy knowledge graph pairs approach in decision making 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems M-CFIS-FKG Knowledge graph Fuzzy knowledge graph FKG-pairs Approximate reasoning Decision making Van Hai, Pham aut Tuan, Tran Manh (orcid)0000-0002-1117-7253 aut Lan, Luong Thi Hong aut Chuan, Pham Minh aut Son, Le Hoang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 18 vom: 29. Apr., Seite 26505-26534 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:18 day:29 month:04 pages:26505-26534 https://doi.org/10.1007/s11042-022-13067-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 18 29 04 26505-26534 |
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10.1007/s11042-022-13067-9 doi (DE-627)OLC207906052X (DE-He213)s11042-022-13067-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Long, Cu Kim verfasserin aut A novel fuzzy knowledge graph pairs approach in decision making 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems M-CFIS-FKG Knowledge graph Fuzzy knowledge graph FKG-pairs Approximate reasoning Decision making Van Hai, Pham aut Tuan, Tran Manh (orcid)0000-0002-1117-7253 aut Lan, Luong Thi Hong aut Chuan, Pham Minh aut Son, Le Hoang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 18 vom: 29. Apr., Seite 26505-26534 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:18 day:29 month:04 pages:26505-26534 https://doi.org/10.1007/s11042-022-13067-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 18 29 04 26505-26534 |
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Long, Cu Kim Van Hai, Pham Tuan, Tran Manh Lan, Luong Thi Hong Chuan, Pham Minh Son, Le Hoang |
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Long, Cu Kim |
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10.1007/s11042-022-13067-9 |
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a novel fuzzy knowledge graph pairs approach in decision making |
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A novel fuzzy knowledge graph pairs approach in decision making |
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Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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A novel fuzzy knowledge graph pairs approach in decision making |
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Van Hai, Pham Tuan, Tran Manh Lan, Luong Thi Hong Chuan, Pham Minh Son, Le Hoang |
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