基于超球和ASSRFOA的多生支持向量机  

Multiple Birth Support Vector Machine Based on Hypersphere and ASSRFOA

作　　者：莫源乐 朱嘉静 刘勇国 张云 李巧勤[1] MO Yuan-Le;ZHU Jia-Jing;LIU Yong-Guo;ZHANG Yun;LI Qiao-Qin(Knowledge and Data Engineering Laboratory of Chinese Medicine,School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China)

机构地区：[1]电子科技大学信息与软件工程学院、中医知识与数据工程实验室,成都610054

出　　处：《计算机系统应用》2023年第9期43-52,共10页Computer Systems & Applications

基　　金：国家自然科学基金(62202084);国家科技基础资源调查专项(2022FY102002);中国博士后科学基金(2021M690028);中央高校基本业务费(ZYGX2021YGLH012,ZYGX2021J020);四川省自然科学基金(2022NSFSC0883,2022NSFSC0958);四川省重点研发计划(2022YFS0059,2023YFS0338)

摘　　要：支持向量机(support vector machine,SVM)是一种基于结构风险最小化的机器学习方法,能够有效解决分类问题.但随着研究问题的复杂化,现实的分类问题往往是多分类问题,而SVM仅能用于处理二分类任务.针对这个问题,一对多策略的多生支持向量机(multiple birth support vector machine,MBSVM)能够以较低的复杂度实现多分类,但缺点在于分类精度较低.本文对MBSVM进行改进,提出了一种新的SVM多分类算法:基于超球(hypersphere)和自适应缩小步长果蝇优化算法(fruit fly optimization algorithm with adaptive step size reduction,ASSRFOA)的MBSVM,简称HA-MBSVM.通过拟合超球得到的信息,先进行类别划分再构建分类器,并引入约束距离调节因子来适当提高分类器的差异性,同时采用ASSRFOA求解二次规划问题,HA-MBSVM可以更好地解决多分类问题.我们采用6个数据集评估HA-MBSVM的性能,实验结果表明HA-MBSVM的整体性能优于各对比算法.Support vector machine(SVM)is a machine learning method based on structural risk minimization and can solve classification problems.However,with the complexity of research problems,the real classification problems are often multi-classification ones,whereas SVM can only be adopted to deal with binary classification tasks.To this end,the multiple birth support vector machine(MBSVM)combined with the one-against-all strategy can realize multiclassification with low complexity,but the classification accuracy is low.This study improves MBSVM and proposes a new SVM multi-classification algorithm which is a multiple birth support vector machine based on the hypersphere and fruit fly optimization algorithm with adaptive step size reduction(ASSRFOA).The algorithm is referred to as HAMBSVM.Through the information obtained from hypersphere fitting,firstly all classes are divided into several blocks and then classifiers are constructed for each class.The constraint distance regulation factor is introduced to properly improve the difference of the classifiers.At the same time,ASSRFOA is employed to solve the quadratic programming problems and HA-MBSVM can better solve the multi-classification problems.Six datasets are utilized to evaluate the performance of HA-MBSVM.The experimental results show that the overall performance of HA-MBSVM is better than that of the comparison algorithms.

关 键 词：超球 多生支持向量机 多分类 自适应缩小步长 果蝇优化算法 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]