多类支持向量机(一对一) [英] Multi-Class SVM( one versus all)

问题描述

我知道，对于多类SVM，LIBSVM仅允许一对一分类.但是，我想对它进行一些微调以执行一个针对所有对象的分类.我试图在下面执行全部反对.这是正确的方法吗?

I know that LIBSVM only allows one-vs-one classification when it comes to multi-class SVM. However, I would like to tweak it a bit to perform one-against-all classification. I have tried to perform one-against-all below. Is this the correct approach?

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.

I might have done some mistakes. I would like to hear some feedback. Thanks.

第二部分: 正如grapet所说: 我需要进行汇总汇总(或作为简化解决方案投票)以得出最终答案.我不确定该怎么做.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.

Second Part: As grapeot said : I need to do Sum-pooling (or voting as a simplified solution) to come up with the final answer. I am not sure how to do it. I need some help on it; I saw the python file but still not very sure. I need some help.

推荐答案

%# 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

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