多类SVM(一对一)

Question

我知道LIBSVM在多类SVM方面只允许一对一分类.但是,我想稍微调整它以执行一对一的分类.我试图在下面进行一对一的比赛.这是正确的方法吗？

代码:

TrainLabel;TrainVec;TestVec;TestLaBel;
u=unique(TrainLabel);
N=length(u);
if(N>2)
    itr=1;
    classes=0;
    while((classes~=1)&&(itr<=length(u)))
        c1=(TrainLabel==u(itr));
        newClass=c1;
        model = svmtrain(TrainLabel, TrainVec, '-c 1 -g 0.00154'); 
        [predict_label, accuracy, dec_values] = svmpredict(TestLabel, TestVec, model);
        itr=itr+1;
    end
itr=itr-1;
end

我可能犯了一些错误.我想听听一些反馈.谢谢.

第二部分:正如葡萄藤所说:我需要做汇总(或投票作为简化解决方案)来得出最终答案.我不知道该怎么做.我需要一些帮助; 我看到了python文件,但仍然不太确定.我需要一些帮助.

Answer 1

%# Fisher Iris dataset
load fisheriris
[~,~,labels] = unique(species);   %# labels: 1/2/3
data = zscore(meas);              %# scale features
numInst = size(data,1);
numLabels = max(labels);

%# split training/testing
idx = randperm(numInst);
numTrain = 100; numTest = numInst - numTrain;
trainData = data(idx(1:numTrain),:);  testData = data(idx(numTrain+1:end),:);
trainLabel = labels(idx(1:numTrain)); testLabel = labels(idx(numTrain+1:end));
%# train one-against-all models
model = cell(numLabels,1);
for k=1:numLabels
    model{k} = svmtrain(double(trainLabel==k), trainData, '-c 1 -g 0.2 -b 1');
end

%# get probability estimates of test instances using each model
prob = zeros(numTest,numLabels);
for k=1:numLabels
    [~,~,p] = svmpredict(double(testLabel==k), testData, model{k}, '-b 1');
    prob(:,k) = p(:,model{k}.Label==1);    %# probability of class==k
end

%# predict the class with the highest probability
[~,pred] = max(prob,[],2);
acc = sum(pred == testLabel) ./ numel(testLabel)    %# accuracy
C = confusionmat(testLabel, pred)                   %# confusion matrix