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基于粒子群优化算法的支持向量机参数选择
引用本文:贺心皓,罗旭. 基于粒子群优化算法的支持向量机参数选择[J]. 计算机系统应用, 2019, 28(8): 241-245
作者姓名:贺心皓  罗旭
作者单位:成都信息工程大学 通信工程学院,成都,610225;成都信息工程大学 通信工程学院,成都,610225
摘    要:由于支持向量机的主要参数的选择能够在很大程度上影响分类性能和效果,并且目前参数优化缺乏理论指导,提出一种粒子群优化算法以优化支持向量机参数的方法.该方法通过引入非线性递减惯性权值和异步线性变化的学习因子策略来改善标准粒子群算法的后期收敛速度慢、易陷入局部最优的缺陷.实验结果表明,相对于标准粒子群算法,本方法在参数优化方面具有良好的鲁棒性、快速收敛和全局搜索能力,具有更高的分类精确度和效率.

关 键 词:支持向量机  粒子群优化算法  SVM参数优化  惯性权值非线性递减  异步变化学习因子
收稿时间:2019-01-24
修稿时间:2019-02-26

Support Vector Machine Parameter Selection Based on Particle Swarm Optimization Algorithm
HE Xin-Hao and LUO Xu. Support Vector Machine Parameter Selection Based on Particle Swarm Optimization Algorithm[J]. Computer Systems& Applications, 2019, 28(8): 241-245
Authors:HE Xin-Hao and LUO Xu
Affiliation:School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China and School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Abstract:Since the selection of the main parameters of the support vector machine can affect the classification performance and effect to a large extent, and the current parameter optimization lacks theoretical guidance, a particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. This method improves the shortcomings of the standard particle swarm optimization algorithm with slow convergence rate and easy to fall into local optimum by introducing nonlinear decreasing inertia weight and asynchronous linear variation learning factor strategy. The experimental results show that compared with the standard particle swarm optimization algorithm, the proposed method has good robustness, fast convergence and global search ability in parameter optimization, and has higher classification accuracy and efficiency.
Keywords:support vector machine  particle swarm optimization algorithm  SVM parameter optimization  inertia weight nonlinear decrement  asynchronous change learning factor
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