基于改进蜂群算法优化的支持向量机研究与应用

Research and application of Support Vector Machine based on improved Artificial Bee Colony algorithm

支持向量机是一种应用广泛的机器学习方法,其分类性能主要取决于相关模型参数的选择。本文提出了一种改进的人工蜂群算法来优化支持向量机的参数,并将其应用于人体活动数据识别。在标准数据集上测试,与基本人工群体算法、遗传算法和粒子群算法等优化算法相比,改进蜂群算法优化的支持向量机可以获得更高的分类准确率。验证了改进人工蜂群算法的有效性。利用改进人工蜂群算法优化的支持向量机对人体活动数据进行分类识别,结果显示该方法具有较高的分类准确率,说明本文所提方法具有实用性。

Support Vector Machine is the widely used machine learning method in which the classification performance mainly depends on the selection of relevant model parameters.In this paper,an improved Artificial Bee Colony algorithm is proposed to optimize the parameters of Support Vector Machine,and is applied to human activity data recognition.When tested on the standard data set,the SVM optimized by the improved Artificial Bee Colony algorithm can obtain higher classification accuracy compared with the basic artificial population algorithm,genetic algorithm and particle swarm optimization.The effectiveness of the improved Artificial Bee Colony algorithm is verified.The Support Vector Machine optimized by improved Artificial Bee Colony algorithm is used to classify and recognize human activity data.The results show that the proposed method has a high classification accuracy,which indicates that the proposed method is practical.