1 简介
为了提高核极限学习机(ELM)的分类正确率,采用哈里斯鹰算法(HHO)对惩罚系数,宽度参数两个参数进行优化.首先,根据乳腺良恶性肿瘤数据库训练集并利用哈里斯鹰算法优化核极限学习机;然后,通过HHO-ELM和ELM对测试集进行分类诊断;最后,对比分析HHO-ELM和ELM的分类性能,测试结果表明,HHO-ELM的总体诊断正确率相较于ELM提高了10%,且恶性肿瘤的诊断正确率明显优于ELM.
2 部分代码
function [fbst, xbst, performance] = hho( objective, d, lmt, n, T, S)%Harris hawks optimization algorithm% inputs:% objective - function handle, the objective function% d - scalar, dimension of the optimization problem% lmt - d-by-2 matrix, lower and upper constraints of the decision varable% n - scalar, swarm size% T - scalar, maximum iteration% S - scalar, times of independent runs% data: -05-09% author: elkman, /ElkmanY/%% Levy flightbeta = 1.5;sigma = ( gamma(1+beta)*sin(pi*beta/2)/gamma((1+beta)/2)*beta*2^((beta-1)/2) ).^(1/b
