基于GA-IPSO-BSVM算法的新浪微博评论信息分类

针对新浪微博评论信息准确分类问题, 本文基于遗传算法(genetic algorithm, GA)、粒子群算法(particle swarm optimization, PSO)和支持向量机(support vector machine, SVM)算法, 提出一种改进GA-IPSO-BSVM (genetic algorithm-improved particle swarm optimization-balanced support vector machine)的分类模型, 以实现提升新浪微博评论信息分类的准确性和收敛性. 首先, 为了有效提升算法的收敛速度, 并高效节省计算资源, 该模型在迭代前期引入GA的淘汰机制, 删除大量低速粒子. 其次, 在迭代中期, 为了避免算法陷入局部最优解, 改进PSO中粒子关系的拓扑结构, 采用K均值聚类(K-means)算法对粒子群进行聚类分区, 将各粒子群体在所属社区中进行粒子群迭代, 选出各个区域中优秀粒子. 再次, 在迭代后期, 将所有区域优秀粒子组合成优秀粒子群体, 并将该群体进行迭代, 得出全局最优解. 从次, 结合GA和IPSO对BSVM进行超参数优化, 提升分类准确率. 最后, 利用所提出的GA-IPSO-BSVM模型对于新浪微博评论信息进行分类预测验证. 经实验结果表明, 该分类模型应用于新浪微博信息分类的准确度优于其他基准模型.

Classification Model of Sina Microblog Comment Information Based on GA-IPSO-BSVM

To accurately classify Sina microblog comment information, this study proposes an improved genetic algorithm-improved particle swarm optimization-balanced support vector machine (GA-IPSO-BSVM) classification model to enhance the accuracy and convergence of classifying Sina microblog comment information. Firstly, to effectively improve the algorithm convergence speed and efficiently save computational resources, this model introduces the elimination mechanism of the GA in the early iteration to remove a large number of low-speed particles. Secondly, to avoid the algorithm being trapped in local optima and improve the topology of particle relations in PSO, this study utilizes a K-means clustering algorithm to perform cluster partition of particle swarms in the middle of the iteration. The particle swarms are iterated in the communities and excellent particles are selected in each community. Thirdly, all excellent particles in the communities are combined into an excellent particle swarm that is iterated to derive the global optimal solution in the late iteration. Fourthly, the hyperparameter optimization of BSVM is performed by combining GA with IPSO to enhance classification accuracy. Finally, the proposed GA-IPSO-BSVM model is used for verifying the classification and prediction of Sina microblog comment information. The experimental results demonstrate the superiority of the proposed classification model over other benchmark models applied to Sina microblog comment information classification in terms of accuracy improvement.