关键词: 随机森林, 量子粒子群优化, 支持向量机, 特征选择, 鲁棒性

Abstract: Too many feature attributes and the optimization of internal parameters are important factors that affect the generalization ability of Support Vector Machine(SVM)models.For the above two problems,to improve the generalization ability and classification accuracy of the SVM model,Random Forest(RF)algorithm and Quantum Particle Swarm Optimization algorithm(QPSO)were combined to optimize the SVM model.The importance of each feature was calculated with RF algorithm,and the features with high importance were selected as a feature subset through feature selection.Using the global search capability of QPSO algorithm,the optimal kernel function parameter  σ and penalty factor C of SVM model were found.The optimal parameters were applied to SVM model for classification prediction.Experimental simulation results showed that compared with other machine learning algorithms,the proposed model had higher training accuracy and prediction accuracy,which also confirmed that the model had better robustness and generalization performance.

Key words: random forest, quantum particle swarm optimization, support vector machine, feature selection, robustness