【SVM分类】基于风驱动算法优化支持向量机实现数据分类附matlab代码
1 简介
支持向量机是利用已知数据类别的样本为训练样本，寻找同类数据的空间聚集特征，从而对测试样本进行分类验证，通过验证可将分类错误的数据进行更正。本文以体检数据为数据背景，首先通过利用因子分析将高维数据进行降维，由此将所有指标整合成几个综合性指标；为降低指标之间的衡量标准所引起的误差，本文利用 MATLAB软件将数据进行归一化处理，结合聚类分析将数据分类；最后本文利用最小二乘支持向量机分类算法进行分类验证，从而计算出数据分类的准确率，并验证了数据分类的准确性和合理性。
2 部分代码
%--------------------------------------------------------------tic;clear;close all;clc;format long g;delete('WDOoutput.txt');delete('WDOpressure.txt');delete('WDOposition.txt');fid=fopen('WDOoutput.txt','a');%--------------------------------------------------------------% User defined WDO parameters:param.popsize = 20; % population size.param.npar = 5; % Dimension of the problem.param.maxit = 500; % Maximum number of iterations.param.RT = 3; % RT coefficient.param.g = 0.2; % gravitational constant.param.alp = 0.4; % constants in the update eq.param.c = 0.4; % coriolis effect.maxV = 0.3; % maximum allowed speed.dimMin = -5; % Lower dimension boundary.dimMax= 5; % Upper dimension boundary.%---------------------------------------------------------------% Initialize WDO population, position and velocity:% Randomize population in the range of [-1, 1]:pos = 2*(rand(param.popsize,param.npar)-0.5);% Randomize velocity:vel = maxV * 2 * (rand(param.popsize,param.npar)-0.5);%---------------------------------------------------------------% Evaluate initial population: (Sphere Function)for K=1:param.popsize,x = (dimMax - dimMin) * ((pos(K,:)+1)./2) + dimMin;pres(K,:) = sum (x.^2);end%----------------------------------------------------------------% Finding best air parcel in the initial population :[globalpres,indx] = min(pres);globalpos = pos(indx,:);minpres(1) = min(pres); % minimum pressure%-----------------------------------------------------------------% Rank the air parcels:[sorted_pres rank_ind] = sort(pres);% Sort the air parcels:pos = pos(rank_ind,:);keepglob(1) = globalpres;%-----------------------------------------------------------------% Start iterations :iter = 1; % iteration counterfor ij = 2:param.maxit,% Update the velocity:for i=1:param.popsize% choose random dimensions:a = randperm(param.npar);% choose velocity based on random dimension:velot(i,:) = vel(i,a);vel(i,:) = (1-param.alp)*vel(i,:)-(param.g*pos(i,:))+ ...abs(1-1/i)*((globalpos-pos(i,:)).*param.RT)+ ...(param.c*velot(i,:)/i);end% Check velocity:vel = min(vel, maxV);vel = max(vel, -maxV);% Update air parcel positions:pos = pos + vel;pos = min(pos, 1.0);pos = max(pos, -1.0);% Evaluate population: (Pressure)for K=1:param.popsize,x = (dimMax - dimMin) * ((pos(K,:)+1)./2) + dimMin;pres(K,:) = sum (x.^2);end%----------------------------------------------------% Finding best particle in population[minpres,indx] = min(pres);minpos = pos(indx,:); % min location for this iteration%----------------------------------------------------% Rank the air parcels:[sorted_pres rank_ind] = sort(pres);% Sort the air parcels position, velocity and pressure:pos = pos(rank_ind,:);vel = vel(rank_ind,:);pres = sorted_pres;% Updating the global best:better = minpres < globalpres;if betterglobalpres = minpres % initialize global minimumglobalpos = minpos;end% Keep a record of the progress:keepglob(ij) = globalpres;save WDOposition.txt pos -ascii -tabs;end%Save values to the final file.pressure = transpose(keepglob);save WDOpressure.txt pressure -ascii -tabs;%END%-----------------------------------------------------
3 运行结果
4 参考文献
[1]张烨, 黄伟. 基于天牛群算法优化SVM的磨煤机故障诊断.