Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Autor:	Konstantinas Korovkinas, Paulius Danėnas, Gintautas Garšva
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	particle swarm optimization support vector machine textual data classification Analysis QA299.6-433
Zdroj:	Nonlinear Analysis, Vol 25, Iss 2 (2020)
Druh dokumentu:	article
ISSN:	1392-5113 2335-8963
DOI:	10.15388/namc.2020.25.16517
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/242fc993b5ea4bc28ad6ee0de29c105e Zobrazit plný text záznamu View record in DOAJ