基于改进型人工鱼群算法的支持向量机参数优化

Support Vector Machine Parameter Optimization Based on Improved Artificial Fish Swarm Algorithm

摘要:

支持向量机在高维度、小样本情况下具有独特优势,但同时支持向量机的参数优化极大制约了其分类效果,目前参数优化缺乏系统的理论指导；针对传统DAG-SVM训练分类器较多,训练耗时长,分类效果受到结构排序的影响,提出了一种基于“1 vs R”策略的改进型算法；针对 SVM传统参数优化方式耗时大,优化精度不高,提出了改进型人工鱼群算法；最后结合1 vs R-DAG支持向量机算法与改进型人工鱼群算法,得到一种新的改进型支持向量机算法；仿真对比实验证实,对支持向量机的参数优化是有效可行的。

关键词:支持向量机;人工鱼群算法;参数优化;有向无环图

Abstract:

Support vector machine(SVM) algorithm has much more advantages than other classify algorithm under high-dimensional, small sample and multi-class situation. But at the same time, the parameters optimization has been one of the main factors restricting SVM effect and there is no clear theory to guide it. For original DAG-SVM algorithm’s long time cost and randomness, an improved algorithm has been proposed；For traditional SVM parameter optimization’s large time consuming and unsatisfactory results, an improved artificial fish swarm algorithm has been proposed；Finally,an improved support vector machine algorithm combined 1vsR-DAG-SVM and IAFSA has been proposed. Simulation experiments confirm that the SVM parameter optimization proposed in this paper is feasible and effective.

Key words:support vector machine; artificial fish swarm algorithm; parameter optimization; directed acyclic graph