基于改进磷虾群算法的SVDD参数优化

Parameter optimization for SVDD based on improved krill herd algorithm

支持向量数据描述(SVDD)是构造单类数据描述的分类算法,惩罚参数C和核参数σ作为影响SVDD分类效果的关键,其合理选取一直是个难点。针对这一问题,提出了一种基于改进磷虾群算法的SVDD参数优化算法(IKH-SVDD)。依据仿真实验,分析参数C和σ对描述边界的影响;引入磷虾群算法并分析其优劣,通过在随机扩散行为中定义扰动因子,增强算法的全局搜索能力;将一种新的精英选择和保留策略引入迭代过程,提高算法的收敛精度;将改进的磷虾群算法引入SVDD参数优化过程,构建了IKH-SVDD参数优化模型。基于UCI标准数据库进行实验并与其他几种参数优化算法进行比较,结果表明了IKH-SVDD算法具有更高的分类准确性。

Support Vector Data Description（SVDD）is a classification algorithm for constructing one-class data description. Penalty parameter C and kernel parameter σ are two key points to affect the classification performance of SVDD and it has always been a difficulty to select reasonably. Focused on this problem, a parameter optimization algorithm of SVDD based on improved krill algorithm is proposed. Firstly, this paper analyzes the C and σ parameters＇ influence on learning models of SVDD through simulation experiment; Then, this paper introduces the krill herd algorithm and analyzes its merits and demerits, defines the disturbance factor in random diffusion behaviors to enhance the global exploratory; Furthermore, a new elitist election and reserving strategy is introduced into the iterative process to improve the accuracy of convergence; Finally, this paper introduces the improved krill herd algorithm into the parameters optimization process of SVDD and establishes the IKH-SVDD parameters optimization model. Simulation with the UCI benchmark datasets indicates that KH-SVDD has better classification accuracy than the current parameter optimization algorithm.