A hybrid method based on <ce:italic>F</ce:italic>-transform for robust estimators
Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yoon, Jin Hee [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: The effect of damping components on the interfacial dynamics and tribological behavior of high-speed train brakes - Wu, Y.K. ELSEVIER, 2021, the treatment of uncertainity in artificial intelligence, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:104 ; year:2019 ; pages:75-83 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.ijar.2018.10.003 |
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ELV044971699 |
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| 520 | |a Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. | ||
| 520 | |a Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. | ||
| 650 | 7 | |a Genetic Algorithm (<ce:italic>GA</ce:italic>) |2 Elsevier | |
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| 650 | 7 | |a <ce:italic>F</ce:italic>-transform |2 Elsevier | |
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| 700 | 1 | |a Seo, Kisung |4 oth | |
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10.1016/j.ijar.2018.10.003 doi GBV00000000000431.pica (DE-627)ELV044971699 (ELSEVIER)S0888-613X(18)30314-1 DE-627 ger DE-627 rakwb eng 530 VZ 33.12 bkl 50.36 bkl 53.79 bkl Yoon, Jin Hee verfasserin aut A hybrid method based on <ce:italic>F</ce:italic>-transform for robust estimators 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Genetic Algorithm (<ce:italic>GA</ce:italic>) Elsevier Orthogonal distance regression (<ce:italic>ODR</ce:italic>) Elsevier Optimization algorithm Elsevier Robust estimators Elsevier <ce:italic>M</ce:italic>-estimators Elsevier <ce:italic>F</ce:italic>-transform Elsevier Kyeong, Deokhwan oth Seo, Kisung oth Enthalten in Elsevier Science Wu, Y.K. ELSEVIER The effect of damping components on the interfacial dynamics and tribological behavior of high-speed train brakes 2021 the treatment of uncertainity in artificial intelligence Amsterdam [u.a.] (DE-627)ELV005830974 volume:104 year:2019 pages:75-83 extent:9 https://doi.org/10.1016/j.ijar.2018.10.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.12 Akustik VZ 50.36 Technische Akustik VZ 53.79 Elektroakustik Tonstudiotechnik VZ AR 104 2019 75-83 9 |
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10.1016/j.ijar.2018.10.003 doi GBV00000000000431.pica (DE-627)ELV044971699 (ELSEVIER)S0888-613X(18)30314-1 DE-627 ger DE-627 rakwb eng 530 VZ 33.12 bkl 50.36 bkl 53.79 bkl Yoon, Jin Hee verfasserin aut A hybrid method based on <ce:italic>F</ce:italic>-transform for robust estimators 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Genetic Algorithm (<ce:italic>GA</ce:italic>) Elsevier Orthogonal distance regression (<ce:italic>ODR</ce:italic>) Elsevier Optimization algorithm Elsevier Robust estimators Elsevier <ce:italic>M</ce:italic>-estimators Elsevier <ce:italic>F</ce:italic>-transform Elsevier Kyeong, Deokhwan oth Seo, Kisung oth Enthalten in Elsevier Science Wu, Y.K. ELSEVIER The effect of damping components on the interfacial dynamics and tribological behavior of high-speed train brakes 2021 the treatment of uncertainity in artificial intelligence Amsterdam [u.a.] (DE-627)ELV005830974 volume:104 year:2019 pages:75-83 extent:9 https://doi.org/10.1016/j.ijar.2018.10.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.12 Akustik VZ 50.36 Technische Akustik VZ 53.79 Elektroakustik Tonstudiotechnik VZ AR 104 2019 75-83 9 |
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10.1016/j.ijar.2018.10.003 doi GBV00000000000431.pica (DE-627)ELV044971699 (ELSEVIER)S0888-613X(18)30314-1 DE-627 ger DE-627 rakwb eng 530 VZ 33.12 bkl 50.36 bkl 53.79 bkl Yoon, Jin Hee verfasserin aut A hybrid method based on <ce:italic>F</ce:italic>-transform for robust estimators 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Genetic Algorithm (<ce:italic>GA</ce:italic>) Elsevier Orthogonal distance regression (<ce:italic>ODR</ce:italic>) Elsevier Optimization algorithm Elsevier Robust estimators Elsevier <ce:italic>M</ce:italic>-estimators Elsevier <ce:italic>F</ce:italic>-transform Elsevier Kyeong, Deokhwan oth Seo, Kisung oth Enthalten in Elsevier Science Wu, Y.K. ELSEVIER The effect of damping components on the interfacial dynamics and tribological behavior of high-speed train brakes 2021 the treatment of uncertainity in artificial intelligence Amsterdam [u.a.] (DE-627)ELV005830974 volume:104 year:2019 pages:75-83 extent:9 https://doi.org/10.1016/j.ijar.2018.10.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.12 Akustik VZ 50.36 Technische Akustik VZ 53.79 Elektroakustik Tonstudiotechnik VZ AR 104 2019 75-83 9 |
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10.1016/j.ijar.2018.10.003 doi GBV00000000000431.pica (DE-627)ELV044971699 (ELSEVIER)S0888-613X(18)30314-1 DE-627 ger DE-627 rakwb eng 530 VZ 33.12 bkl 50.36 bkl 53.79 bkl Yoon, Jin Hee verfasserin aut A hybrid method based on <ce:italic>F</ce:italic>-transform for robust estimators 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Genetic Algorithm (<ce:italic>GA</ce:italic>) Elsevier Orthogonal distance regression (<ce:italic>ODR</ce:italic>) Elsevier Optimization algorithm Elsevier Robust estimators Elsevier <ce:italic>M</ce:italic>-estimators Elsevier <ce:italic>F</ce:italic>-transform Elsevier Kyeong, Deokhwan oth Seo, Kisung oth Enthalten in Elsevier Science Wu, Y.K. ELSEVIER The effect of damping components on the interfacial dynamics and tribological behavior of high-speed train brakes 2021 the treatment of uncertainity in artificial intelligence Amsterdam [u.a.] (DE-627)ELV005830974 volume:104 year:2019 pages:75-83 extent:9 https://doi.org/10.1016/j.ijar.2018.10.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.12 Akustik VZ 50.36 Technische Akustik VZ 53.79 Elektroakustik Tonstudiotechnik VZ AR 104 2019 75-83 9 |
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10.1016/j.ijar.2018.10.003 doi GBV00000000000431.pica (DE-627)ELV044971699 (ELSEVIER)S0888-613X(18)30314-1 DE-627 ger DE-627 rakwb eng 530 VZ 33.12 bkl 50.36 bkl 53.79 bkl Yoon, Jin Hee verfasserin aut A hybrid method based on <ce:italic>F</ce:italic>-transform for robust estimators 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. Genetic Algorithm (<ce:italic>GA</ce:italic>) Elsevier Orthogonal distance regression (<ce:italic>ODR</ce:italic>) Elsevier Optimization algorithm Elsevier Robust estimators Elsevier <ce:italic>M</ce:italic>-estimators Elsevier <ce:italic>F</ce:italic>-transform Elsevier Kyeong, Deokhwan oth Seo, Kisung oth Enthalten in Elsevier Science Wu, Y.K. ELSEVIER The effect of damping components on the interfacial dynamics and tribological behavior of high-speed train brakes 2021 the treatment of uncertainity in artificial intelligence Amsterdam [u.a.] (DE-627)ELV005830974 volume:104 year:2019 pages:75-83 extent:9 https://doi.org/10.1016/j.ijar.2018.10.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.12 Akustik VZ 50.36 Technische Akustik VZ 53.79 Elektroakustik Tonstudiotechnik VZ AR 104 2019 75-83 9 |
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Enthalten in The effect of damping components on the interfacial dynamics and tribological behavior of high-speed train brakes Amsterdam [u.a.] volume:104 year:2019 pages:75-83 extent:9 |
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Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. |
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Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. |
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Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L 1 , L 1 − L 2 and Fair have been used as existing robust methods. L 2 , which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in R M S E , M A D and M A P E to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L 1 , L 1 − L 2 and Fair. |
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