Particle swarm optimization for linear support vector machines based classifier selection
Articles
Gintautas Garšva
Vilnius University, Lithuania
Paulius Danėnas
Vilnius University, Lithuania
Published 2014-01-20
https://doi.org/10.15388/NA.2014.1.2
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Keywords

particle swarm optimization
linear SVM
support vector machines
machine learning
classification

How to Cite

Garšva G. and Danėnas P. (2014) “Particle swarm optimization for linear support vector machines based classifier selection”, Nonlinear Analysis: Modelling and Control, 19(1), pp. 26-42. doi: 10.15388/NA.2014.1.2.

Abstract

Particle swarm optimization is a metaheuristic technique widely applied to solve various optimization problems as well as parameter selection problems for various classification techniques. This paper presents an approach for linear support vector machines classifier optimization combining its selection from a family of similar classifiers with parameter optimization. Experimental results indicate that proposed heuristics can help obtain competitive or even better results compared to similar techniques and approaches and can be used as a solver for various classification tasks.

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