The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach
The problem of effective intellectual analysis in the case of handling short datasets is topical in various application areas. Such problems arise in medicine, economics, materials science, science, etc. This paper deals with a new additive input-doubling method designed by the authors for processin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/4/612 |
| _version_ | 1797538596103651328 |
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| author | Ivan Izonin Roman Tkachenko Nataliya Shakhovska Nataliia Lotoshynska |
| author_facet | Ivan Izonin Roman Tkachenko Nataliya Shakhovska Nataliia Lotoshynska |
| author_sort | Ivan Izonin |
| collection | DOAJ |
| description | The problem of effective intellectual analysis in the case of handling short datasets is topical in various application areas. Such problems arise in medicine, economics, materials science, science, etc. This paper deals with a new additive input-doubling method designed by the authors for processing short and very short datasets. The main steps of the method should include the procedure of data augmentation within the existing dataset both in rows and columns (without training), the use of nonlinear SVR to implement the training procedure, and the formation of the result based on the author’s procedure. The authors show that the developed data augmentation procedure corresponds to the principles of axial symmetry. The training and application procedures of the method developed are described in detail, and two algorithmic implementations are presented. The optimal parameters of the method operation were selected experimentally. The efficiency of its work during the processing of short datasets for solving the prediction task was established experimentally by comparison with other methods of this class. The highest prediction accuracy based on both proposed algorithmic implementations of a method among all of the investigated ones was defined. The main areas of application of the developed method are described, and its shortcomings and prospects of further research are given. |
| first_indexed | 2024-03-10T12:33:40Z |
| format | Article |
| id | doaj.art-33c047b985824f469d72e58e18142589 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T12:33:40Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-33c047b985824f469d72e58e181425892023-11-21T14:24:42ZengMDPI AGSymmetry2073-89942021-04-0113461210.3390/sym13040612The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data ApproachIvan Izonin0Roman Tkachenko1Nataliya Shakhovska2Nataliia Lotoshynska3Department of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Str., 5, 79905 Lviv, UkraineDepartment of Publishing Information Technologies, Lviv Polytechnic National University, S. Bandera Str., 12, 79013 Lviv, UkraineDepartment of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Str., 5, 79905 Lviv, UkraineDepartment of Publishing Information Technologies, Lviv Polytechnic National University, S. Bandera Str., 12, 79013 Lviv, UkraineThe problem of effective intellectual analysis in the case of handling short datasets is topical in various application areas. Such problems arise in medicine, economics, materials science, science, etc. This paper deals with a new additive input-doubling method designed by the authors for processing short and very short datasets. The main steps of the method should include the procedure of data augmentation within the existing dataset both in rows and columns (without training), the use of nonlinear SVR to implement the training procedure, and the formation of the result based on the author’s procedure. The authors show that the developed data augmentation procedure corresponds to the principles of axial symmetry. The training and application procedures of the method developed are described in detail, and two algorithmic implementations are presented. The optimal parameters of the method operation were selected experimentally. The efficiency of its work during the processing of short datasets for solving the prediction task was established experimentally by comparison with other methods of this class. The highest prediction accuracy based on both proposed algorithmic implementations of a method among all of the investigated ones was defined. The main areas of application of the developed method are described, and its shortcomings and prospects of further research are given.https://www.mdpi.com/2073-8994/13/4/612small data approachshort datasetinput-doubling methodmachine learningSVRnonlinear kernels |
| spellingShingle | Ivan Izonin Roman Tkachenko Nataliya Shakhovska Nataliia Lotoshynska The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach Symmetry small data approach short dataset input-doubling method machine learning SVR nonlinear kernels |
| title | The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach |
| title_full | The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach |
| title_fullStr | The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach |
| title_full_unstemmed | The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach |
| title_short | The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach |
| title_sort | additive input doubling method based on the svr with nonlinear kernels small data approach |
| topic | small data approach short dataset input-doubling method machine learning SVR nonlinear kernels |
| url | https://www.mdpi.com/2073-8994/13/4/612 |
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