Support vector machine parameter tuning based on particle swarm optimization metaheuristic
This paper introduces a method for linear support vector machine parameter tuning based on particle swarm optimization metaheuristic, which is used to find the best cost (penalty) parameter for a linear support vector machine to increase textual data classification accuracy. Additionally, majority v...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2020-03-01
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| Series: | Nonlinear Analysis |
| Subjects: | |
| Online Access: | https://www.journals.vu.lt/nonlinear-analysis/article/view/16517 |
| _version_ | 1818335792070656000 |
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| author | Konstantinas Korovkinas Paulius Danėnas Gintautas Garšva |
| author_facet | Konstantinas Korovkinas Paulius Danėnas Gintautas Garšva |
| author_sort | Konstantinas Korovkinas |
| collection | DOAJ |
| description | This paper introduces a method for linear support vector machine parameter tuning based on particle swarm optimization metaheuristic, which is used to find the best cost (penalty) parameter for a linear support vector machine to increase textual data classification accuracy. Additionally, majority voting based ensembling is applied to increase the efficiency of the proposed method. The results were compared with results from our previous research and other authors’ works. They indicate that the proposed method can improve classification performance for a sentiment recognition task. |
| first_indexed | 2024-12-13T14:29:03Z |
| format | Article |
| id | doaj.art-242fc993b5ea4bc28ad6ee0de29c105e |
| institution | Directory Open Access Journal |
| issn | 1392-5113 2335-8963 |
| language | English |
| last_indexed | 2024-12-13T14:29:03Z |
| publishDate | 2020-03-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Nonlinear Analysis |
| spelling | doaj.art-242fc993b5ea4bc28ad6ee0de29c105e2022-12-21T23:41:52ZengVilnius University PressNonlinear Analysis1392-51132335-89632020-03-0125210.15388/namc.2020.25.16517Support vector machine parameter tuning based on particle swarm optimization metaheuristicKonstantinas Korovkinas0Paulius Danėnas1Gintautas Garšva2Vilnius UniversityKaunas University of TechnologyVilnius UniversityThis paper introduces a method for linear support vector machine parameter tuning based on particle swarm optimization metaheuristic, which is used to find the best cost (penalty) parameter for a linear support vector machine to increase textual data classification accuracy. Additionally, majority voting based ensembling is applied to increase the efficiency of the proposed method. The results were compared with results from our previous research and other authors’ works. They indicate that the proposed method can improve classification performance for a sentiment recognition task.https://www.journals.vu.lt/nonlinear-analysis/article/view/16517particle swarm optimizationsupport vector machinetextual data classification |
| spellingShingle | Konstantinas Korovkinas Paulius Danėnas Gintautas Garšva Support vector machine parameter tuning based on particle swarm optimization metaheuristic Nonlinear Analysis particle swarm optimization support vector machine textual data classification |
| title | Support vector machine parameter tuning based on particle swarm optimization metaheuristic |
| title_full | Support vector machine parameter tuning based on particle swarm optimization metaheuristic |
| title_fullStr | Support vector machine parameter tuning based on particle swarm optimization metaheuristic |
| title_full_unstemmed | Support vector machine parameter tuning based on particle swarm optimization metaheuristic |
| title_short | Support vector machine parameter tuning based on particle swarm optimization metaheuristic |
| title_sort | support vector machine parameter tuning based on particle swarm optimization metaheuristic |
| topic | particle swarm optimization support vector machine textual data classification |
| url | https://www.journals.vu.lt/nonlinear-analysis/article/view/16517 |
| work_keys_str_mv | AT konstantinaskorovkinas supportvectormachineparametertuningbasedonparticleswarmoptimizationmetaheuristic AT pauliusdanenas supportvectormachineparametertuningbasedonparticleswarmoptimizationmetaheuristic AT gintautasgarsva supportvectormachineparametertuningbasedonparticleswarmoptimizationmetaheuristic |