我想在 MATLAB 中的一对多支持向量机分类中进行 10倍交叉验证。
我试图以某种方式混合这两个相关的答案：
但由于我是 MATLAB 及其语法的新手，所以直到现在我才设法让它工作。
另一方面，我在LibSVM README 文件中只看到以下几行关于交叉验证的内容，但在那里我找不到任何相关示例：
选项 -v 将数据随机拆分为 n 个部分，并计算它们的交叉验证准确度/均方误差。
有关输出的含义，请参阅 libsvm FAQ。
谁能给我一个 10 倍交叉验证和一对一分类的例子？
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2 回答
15
我们进行交叉验证主要有两个原因：
对于我们感兴趣的第一种情况，该过程涉及k为每个折叠训练模型，然后在整个训练集上训练一个最终模型。我们报告了 k 折的平均准确度。
现在，由于我们使用一对多的方法来处理多类问题，每个模型都包含N支持向量机（每个类一个）。
以下是实现一对多方法的包装函数：
function mdl = libsvmtrain_ova(y, X, opts)
if nargin < 3, opts = ''; end
%# classes
labels = unique(y);
numLabels = numel(labels);
%# train one-against-all models
models = cell(numLabels,1);
for k=1:numLabels
models{k} = libsvmtrain(double(y==labels(k)), X, strcat(opts,' -b 1 -q'));
end
mdl = struct('models',{models}, 'labels',labels);
end
function [pred,acc,prob] = libsvmpredict_ova(y, X, mdl)
%# classes
labels = mdl.labels;
numLabels = numel(labels);
%# get probability estimates of test instances using each 1-vs-all model
prob = zeros(size(X,1), numLabels);
for k=1:numLabels
[~,~,p] = libsvmpredict(double(y==labels(k)), X, mdl.models{k}, '-b 1 -q');
prob(:,k) = p(:, mdl.models{k}.Label==1);
end
%# predict the class with the highest probability
[~,pred] = max(prob, [], 2);
%# compute classification accuracy
acc = mean(pred == y);
end
以下是支持交叉验证的函数：
function acc = libsvmcrossval_ova(y, X, opts, nfold, indices)
if nargin < 3, opts = ''; end
if nargin < 4, nfold = 10; end
if nargin < 5, indices = crossvalidation(y, nfold); end
%# N-fold cross-validation testing
acc = zeros(nfold,1);
for i=1:nfold
testIdx = (indices == i); trainIdx = ~testIdx;
mdl = libsvmtrain_ova(y(trainIdx), X(trainIdx,:), opts);
[~,acc(i)] = libsvmpredict_ova(y(testIdx), X(testIdx,:), mdl);
end
acc = mean(acc); %# average accuracy
end
function indices = crossvalidation(y, nfold)
%# stratified n-fold cros-validation
%#indices = crossvalind('Kfold', y, nfold); %# Bioinformatics toolbox
cv = cvpartition(y, 'kfold',nfold); %# Statistics toolbox
indices = zeros(size(y));
for i=1:nfold
indices(cv.test(i)) = i;
end
end
最后，这里是一个简单的demo来说明用法：
%# laod dataset
S = load('fisheriris');
data = zscore(S.meas);
labels = grp2idx(S.species);
%# cross-validate using one-vs-all approach
opts = '-s 0 -t 2 -c 1 -g 0.25'; %# libsvm training options
nfold = 10;
acc = libsvmcrossval_ova(labels, data, opts, nfold);
fprintf('Cross Validation Accuracy = %.4f%%\n', 100*mean(acc));
%# compute final model over the entire dataset
mdl = libsvmtrain_ova(labels, data, opts);
将其与 libsvm 默认使用的一对一方法进行比较：
acc = libsvmtrain(labels, data, sprintf('%s -v %d -q',opts,nfold));
model = libsvmtrain(labels, data, strcat(opts,' -q'));
于 2012-12-26T14:36:06.013 回答
2
这两个问题之一与 LIBSVM 无关，这可能会让您感到困惑。您应该尝试调整此答案并忽略其他答案。
您应该选择折叠，然后按照链接的问题完成其余部分。假设数据已加载到data标签中labels：
n = size(data,1);
ns = floor(n/10);
for fold=1:10,
if fold==1,
testindices= ((fold-1)*ns+1):fold*ns;
trainindices = fold*ns+1:n;
else
if fold==10,
testindices= ((fold-1)*ns+1):n;
trainindices = 1:(fold-1)*ns;
else
testindices= ((fold-1)*ns+1):fold*ns;
trainindices = [1:(fold-1)*ns,fold*ns+1:n];
end
end
% use testindices only for testing and train indices only for testing
trainLabel = label(trainindices);
trainData = data(trainindices,:);
testLabel = label(testindices);
testData = data(testindices,:)
%# 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(size(testData,1),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
end
于 2012-12-26T07:11:34.130 回答