PVS-PSO-SVR cooperative model and its empirical analysis
In response to the redundancy of high-dimensional random variable information and the shortcom-ings of principal component analysis in dimensionality reduction,the principal variable screening method was used to reduce the dimensionality of high-dimensional random variables.A support vector regres-sion machine model was established using the extracted principal variables For the parameters of the model,an improved particle swarm optimization algorithm was used for optimization selection Construct a collabo-rative model of Principal Variable Screening(PVS),Particle Swarm Optimization(PSO),and Support Vector Regression(SVR)for wine quality prediction.Demonstration experiments shown that the PVS-PSO-SVR collaborative model has significantly improved inspection errors compared to the existing three models(N-CV-SVR model,PCA-CV-SVR model,PVS-CV-SVR model),which indicates that the collaborative model of principal variable selection,particle swarm optimization,and support vector re-gression has a stronger generalization ability and more effective prediction results.
principal variable selectionparticle swarm optimizationsupport vector regression(SVR)quality prediction
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