利用SVM实现一个三类分类问题（转贴）
一．任务要求用SVM求解一个三类分类问题，实验数据为“鸢尾属植物数据集”，核函数为径向基核函数（RBF），误差评测标准为K折交叉确认误差。
二．实验方案
1． 用quadprog函数实现C-SVC来进行分类
——quadprog是matlab中一个求解二次规划的函数，通过适当的参数设置，可以利用quadprog函数实现C-SVC
2． 用matlab自带的SVM工具包来实现分类
——matlab2006版本中集成了SVM工具包，可以通过调用工具包中的svmtrain和svmclassify函数来进行训练和分类
3． 三类问题的分类方法
——将三类问题转化为三个两类问题，分别求出相应的决策函数即可（优点：方法简单易行；缺点：容易形成死区）
三．实验程序
1． 用Quadprog实现
clear all
% Load the data and select features for classification
load fisheriris;
data = meas;
%Get the size of the data
N = size(data,1);
% Extract the Setosa class
groups_temp = ismember(species,'versicolor');%versicolor,virginica,setosa
%convert the group to 1 & -1
groups = 2*groups_temp - ones(N,1);
indices = crossvalind('Kfold', groups);
ErrorMin = 1;
for r=1:1:5
for C=1:1:5
ErrorNum = 0;
for i=1:5
%Use K-fold to get train data and test data
test = (indices == i); train = ~test;
traindata = data(train,:);
traingroup = groups(train,:);
trainlength = length(traingroup);
testdata = data(test,:);
testgroup = groups(test,:);
testlength = length(testgroup);
%Get matrix H of the problem
kfun = [];
for i=1:1:trainlength
for j=1:1:trainlength
%rbf kernel
kfun(i,j)=exp(-1/(r^2)*(traindata(i,:)-traindata(j,:))*(traindata(i,:)-traindata(j,:))');
end
end
%count parameters of quadprog function
H = (traingroup*traingroup').*kfun;
xstart = zeros(trainlength,1);
f = -ones(trainlength,1);
Aeq = traingroup';
beq = 0;
lb = zeros(trainlength,1);
ub = C*ones(trainlength,1);
= quadprog(H,f,[],[],Aeq,beq,lb,ub,xstart);
%Get one of the non-zero part of vector alpha to count b
j = 1;
for i=1:size(alpha)
if(alpha(i)>(1e-5))
SvmClass_temper(j,:) = traingroup(i);
SvmAlpha_temper(j,:) = alpha(i);
SvmVector_temper(j,:)= traindata(i,:);
j = j + 1;
tag = i;
end
end
b=traingroup(tag)-(alpha.*traingroup)'*kfun(:,tag);
%Use the function to test the test data
kk = [];
for i=1:testlength
for j=1:trainlength
kk(i,j)=exp(-1/(r^2)*(testdata(i,:)-traindata(j,:))*(testdata(i,:)-traindata(j,:))');
end
end
%then count the function
f=(alpha.*traingroup)'*kk' + b;
for i=1:length(f)
if(f(i)>(1e-5))
f(i)=1;
else
f(i)=-1;
end
end
for i=1:length(f)
if(testgroup(i)~=f(i))
ErrorNum = ErrorNum + 1;
end
end
end
ErrorRate = ErrorNum / N;
if(ErrorRate<ErrorMin)
SvmClass = SvmClass_temper;
SvmAlpha = SvmAlpha_temper;
SvmVector = SvmVector_temper;
ErrorMin = ErrorRate;
CorrectRate = 1 - ErrorRate;
Coptimal = C;
Roptimal = r;
end
end
end 2． 用SVM工具包实现
clear all
% Load the data and select features for classification
load fisheriris
% data = ;
data=meas;
% Extract the Setosa class
groups = ismember(species,'versicolor');%versicolor,virginica,setosa
% Randomly select training and test sets
index = crossvalind('Kfold',groups);
cp = classperf(groups);
fr=0;
fc=0;
fcorrect=0;
correct5=0;
for r=1:1:10
for c=1:1:100
for i=1:5
test = (index == i); train = ~test;
% Use a RBF support vector machine classifier
% svmStruct = svmtrain(data(train,:),groups(train),'KERNEL_FUNCTION','rbf','kfunargs',5,'boxconstraint',1000,'showplot',true);
% classes = svmclassify(svmStruct,data(test,:),'showplot',true);
svmStruct = svmtrain(data(train,:),groups(train),'KERNEL_FUNCTION','rbf','kfunargs',1/(r^2),'boxconstraint',c);
classes = svmclassify(svmStruct,data(test,:));
% See how well the classifier performed
classperf(cp,classes,test);
% cp.CorrectRate
correct5=correct5+cp.CorrectRate/5;
end
r
c
correct5
if(fcorrect<correct5)
fcorrect=correct5
fr=r
fc=c
end
correct5=0;
end
end 四．实验结果
1． Quadprog实现
(1)类别：versicolor 参数：r(1-10) C(1-100)
运行结果：
CorrectRate =0.9696 Roptimal =1 Coptimal =2
(2)类别：virginica 参数：r(1-10) C(1-100)
运行结果：
CorrectRate =0.9430 Roptimal =1 Coptimal =2
(3)类别：setosa 参数：r(1-10) C(1-100)
运行结果：
CorrectRate =1 Roptimal =1 Coptimal =1
2． SVM工具包实现
(1)类别：versicolor 参数：r(1-5) C(1-50)
运行结果：
CorrectRate =1 Roptimal =2 Coptimal =22
(2)类别：virginica参数：r(1-5) C(1-50)
运行结果：
CorrectRate =0.9867 Roptimal =10 Coptimal =44
(3)类别：setosa 参数：r(1-10) C(1-100)
运行结果：
CorrectRate =1 Roptimal =1 Coptimal =1 无法运行，indices = crossvalind('Kfold', groups);出错，请求帮助！ crossvalind这是个什么函数啊？那个库里有呢？
怎么了
Error: The input character is not valid in MATLAB statements or expressions. 如果只有两种类型的训练数据，能不能分出三类，也就是除了分出这两点之外，能不能把阈值之外的算作第三类，第三类是虚拟的，三是没有特征数据 特征数据
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