基于 DYCORS 算法的 OVA-SVM 参数优化与应用研究

目的 现有的参数优化方法普遍存在时间成本较大、内存占用较大、难以解决高维数据情况、难以找到全局最优解等问题，DYCORS算法可以在节约时间成本和内存的前提下，对高维数据问题也能找到全局最优解，故针对现有参数优化方法存在的问题，提出了针对OVA-SVM模型参数分块优化的YDYCORS算法。方法 OVA-SVM的参数中对模型影响较大的有惩罚参数C、核函数类型k、RBF核函数参数γ、ploy核函数参数d以及迭代终止参数t,由于同时调节5个参数计算量较大，难以找到最优解，而DYCORS算法可以减少迭代次数，对于高维数据问题也同样适用，在DYCORS算法的基础上进行参数分块调节：先调节影响最大的参数C、k、γ,再固定最优参数C、k、γ,调节剩余参数中影响较大的参数d和t,最后同时调节已获得的5个最优参数，如此对参数进行分块调节，提升参数优化的效果。结果 通过MNIST和IRIS两个数据集上的实验结果对比可以发现：运用YDYCORS算法对OVA-SVM参数进行分块调节后，能得到与手动调参和直接用DYCORS同时调节5个参数更高的模型准确率，从而也能进一步提升模型性能。结论 最终实验结果表明：DYC...

Optimization and Application of OVA-SVM Parameters Based on DYCORS Algorithm

Objective The existing parameter optimization methods generally have problems such as large time cost largememory occupation difficulty in solving high-dimensional data and difficulty in finding global optimal solutions. TheDYCORS algorithm can find the global optimal solution for high-dimensional data problems even with the saving of timecost and memory. Therefore in view of the problems existing in the existing parameter optimization methods theYDYCORS algorithm for block optimization of OVA-SVM model parameters was proposed. Methods Among theparameters of OVA-SVM the penalty parameter C the kernel type k the RBF kernel function parameter γ the ploykernel function parameter d and the iteration termination parameter t have a greater impact on the model. Due to thelarge computational effort of adjusting five parameters simultaneously it is difficult to find the optimal solution. TheDYCORS algorithm can also be applied to high-dimensional data problems by reducing the number of iterations and thenthe parameters were adjusted in blocks based on the DYCORS algorithm. The most influential parameters C k and γwere adjusted first then the optimal parameters C k and γ were fixed the more influential parameters d and t amongthe remaining parameters were adjusted and finally the five optimal parameters that had been obtained were adjusted simultaneously so that the parameters were adjusted in blocks to improve the effect of parameter optimization.Results Through the comparison of the experimental results on MNIST and IRIS data sets it can be found that after usingthe YDYCORS algorithm to adjust the parameters of OVA-SVM in blocks the model accuracy can be higher than theaccuracies of manual parameter adjustment and directly using DYCORS to adjust the five parameters at the same time which can also further improve its model performance. Conclusion The final experimental results show that DYCORSalgorithm can effectively solve the problems of OVA-SVM parameter optimization such as high time cost large memoryoccupation difficulty in solving high-dimensional data problems and difficulty in finding the global optimal solution. Inparticular the improved YDYCORS algorithm can further improve the accuracy of the OVA-SVM model and obtain abetter model effect.