Research on parameters optimisation of SVM based on swarm intelligence

Ding, Shifei; Huang, Huajuan

doi:10.1504/ijci.2014.064852

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…At present, these parameters are usually defined artificially based on the specific issues, and the optimal parameter combination is determined by choosing the parameters for many times and comparing with each other. Parameters that are manually set are blind and of low efficiency, so it is needed to adopt swarm intelligence optimization 40 algorithm to improve the parameter choosing of the SVM. At the same time, the design and implementation of PSO algorithm is relatively simple.…”

Section: The Process Of Traffic Fatalities Prediction Based On Psom-svmmentioning

confidence: 99%

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Wang

et al. 2017

Journal of Algorithms & Computational Technology

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Road traffic safety is essential, therefore in order to predict traffic fatalities effectively and promote the harmonious development of transportation, a traffic fatalities prediction model based on support vector machine is established in this paper. The selection of parameters greatly affects the prediction accuracy of support vector machine. Introducing particle swarm optimization can find the optimal parameters and improve the prediction accuracy of support vector machine by parameter optimization. However, standard particle swarm optimization is easy to trap into the local optimum, so that the best parameter solutions cannot be found. Therefore, the mutation operation of the genetic algorithm is introduced into particle swarm optimization, particle swarm with mutation optimization is generated. It expands the search space and makes parameter selection more accurate. This paper predicts fatalities of traffic accident using small samples and nonlinear data. The results show that compared with particle swarm with mutation optimization back propagation neural network prediction model, particle swarm optimization-support vector machine model, support vector machine, back propagation neural network, K Nearest Neighbor (K-NN), and Bayesian network, the prediction model of traffic fatalities based on particle swarm with mutation optimization-support vector machine has higher prediction precision and smaller errors. It is feasible and effective to use particle swarm with mutation optimization to optimize the parameters of support vector machine, and this model can predict the accident more accurately.

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Section: The Process Of Traffic Fatalities Prediction Based On Psom-svmmentioning

confidence: 99%

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Wang

et al. 2017

Journal of Algorithms & Computational Technology

View full text Add to dashboard Cite

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Application of particle swarm optimization in parameter identification of power electronic circuits

Lai

2020

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

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Research on parameters optimisation of SVM based on swarm intelligence

Cited by 2 publications

References 30 publications

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Application of particle swarm optimization in parameter identification of power electronic circuits

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