摘要
网络舆情具有小样本特征,而传统方法预测准确率低,容易得到局部最优。为此,提出一种改进黏菌算法优化支持向量机的网络舆情预测模型ISMA-SVM。引入混沌Circle映射机制提高初始种群的多样性;利用对数非线性调节反馈因子均衡算法全局搜索与局部开发;设计透镜成像对立学习机制对最优个体变异,扩展搜索空间并避免算法陷入局部最优。利用改进黏菌优化算法优化支持向量机模型,构建网络舆情预测模型。以若干舆情热点百度指数作为样本进行实证研究,结果表明,改进模型具有更高的数据拟合度,预测准确度更高。
The network public opinion has small sample characteristics,while the traditional method has lower prediction precision,leading to local optimum.A network public opinion prediction model ISMA-SVM based on improved slime mold algorithm with optimizing support vector machine was proposed.The chaotic circle mapping was introduced to improve the diversity of the initial population.The feedback factor was adjusted in a nonlinear way based on logarithm for balancing global search and local development of the algorithm.The lens imaging opposition learning mechanism was designed to disturb the optimal individual variation,expand the search space and avoid the algorithm falling into local optimization.The support vector machine model was optimized using the improved slime mold optimization algorithm,and the network public opinion prediction model was constructed.Some public opinion hot Baidu indexes were used as the sample to conduct an empirical study.The results show that the improved model has higher data fitting degree and prediction accuracy.
作者
李菲
陈燕
LI Fei;CHEN Yan(Guangxi Key Laboratory of Big Data in Finance and Economics,School of Big Data and Artificial Intelligence,Guangxi University of Finance and Economics,Nanning 530003,China;Guangxi Key Laboratory of Multimedia Communications Network Technology,School of Computer and Electronic Information,Guangxi University,Nanning 530004,China)
出处
《计算机工程与设计》
北大核心
2023年第5期1547-1554,共8页
Computer Engineering and Design
基金
教育部人文社科研究规划基金项目(20YJA740021)
南宁市科学研究与技术开发计划重大基金项目(20211005)
广西自然科学基金项目(2020GXNSFAA159090)
广西多媒体通信与网络技术重点实验室开放基金项目(KLF-2020-04)
统计学广西一流学科建设基金项目(桂教科研[2022]1号)
广西财经大数据重点实验室基金项目(桂科基字[2021]5号)。