基于群体智能优化的MKL-SVM算法及肺结节识别  

MKL-SVM algorithm for pulmonary nodule recognition based on swarm intelligenceoptimization

作　　者：李阳[1] 常佳乐 王宇阳 LI Yang;CHANG Jia-yue;WANG Yu-yang(School of Computer Science and Engineering,Changchun University of Technology,Changchun 130012,China)

机构地区：[1]长春工业大学计算机科学与工程学院,长春130012

出　　处：《工程科学学报》2021年第9期1157-1165,共9页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(61806024);吉林省教育厅十三五科研规划项目(JJKH20181041KJ,JJKH20200680KJ);吉林省科技发展计划项目(20200401103GX)。

摘　　要：针对单核学习支持向量机无法兼顾学习能力与泛化能力以及多核函数参数寻优问题,提出了一种基于群体智能优化的多核学习支持向量机算法.首先,研究了五种单核函数对支持向量机分类性能的影响,进一步提出具有全局性质的多项式核和局部性质的拉普拉斯核凸组合形式的多核学习支持向量机算法;其次,为增加粒子多样性及快速寻优,将粒子群优化算法引入了遗传算法中的杂交操作,并用此改进的群体智能优化算法对多核学习支持向量机进行参数寻优.最后,分别采用深度特征与手工特征作为识别算法的输入,研究表明采用深度特征优于手工特征.故本文采用深度特征作为多核学习支持向量机的输入,以交叉遗传与粒子群混合智能优化算法作为其寻优方式.实验选取合作医院数据集对所提算法进行训练并初步测试,进一步为了验证所提算法的泛化能力,选取公开数据集LUNA16进行测试.实验结果表明,本文算法易于跳出局部最优解,提升了算法的学习能力与泛化能力,具有较优的分类性能.To solve the problem that a single kernel learning support vector machine(SVM)cannot consider the learning and generalization abilities and parameter optimization of the multiple kernel function,a multiple kernel learning support vector machine(MKL-SVM)algorithm based on swarm intelligence optimization was proposed.First,the impact of five single kernel functions on the classification indexes of SVM was discussed.These kernel functions include two global kernel functions—the polynomial and sigmoid kernel functions—and three local kernel functions—the radial basis function,exponential kernel function,and Laplacian kernel function.Next,an MKL-SVM algorithm with a convex combination of a polynomial kernel having global properties and a Laplacian kernel having local properties was proposed.Then,to improve particle diversity to avoid falling into local optimal solutions during the iteration,and to reduce the model’s training time,the crossover operation in the genetic algorithm was introduced into the particle swarm optimization(PSO)algorithm.This improved swarm intelligence optimization was used to optimize the parameters of the MKL-SVM.Finally,deep learning features based on the classical model VGG16 and handcrafted features according to doctors’suggestions were used as inputs for the recognition algorithm.In this algorithm,transfer learning was used to extract deep learning features and principal component analysis was used to reduce computational complexity through dimensionality reduction.The results show that using deep learning features is better than handcrafted features.Therefore,this paper adopts the deep learning features as input for the MKL-SVM algorithm and the hybrid swarm intelligent optimization algorithm of crossover genetic and the PSO algorithm as the optimization method.To verify the generalization ability of the proposed algorithm,the public dataset LUNA16 was selected for testing.The experimental results show that the proposed algorithm is easy to jump out of the local optimal solution

关 键 词：核函数 支持向量机 交叉遗传 粒子群优化 深度特征 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]