Question

我正在做libsvm，如图所示：

numLabels = max(trainlabels);
model = cell(numLabels,1);
for k=1:numLabels
    model{k} = svmtrain(double(trainlabels==k), train_data, '-t 0 -b 1');
end

numTest = size(test_data,1);
prob = zeros(numTest,numLabels);
for k=1:numLabels
    [predicted_label, accuracy, prob_estimates] = svmpredict(double(testlabels==k), test_data, model{k}, '-b 1');
    fprintf('sum(predicted_label==1) = %i \n', sum(predicted_label==1));
    fprintf('sum(testlabels==k) = %i \n', sum(testlabels==k));
    cc = model{k}.Label==1;
    prob(:,k) = prob_estimates(:,cc);    %# probability of class==k
end

%# predict the class with the highest probability
[~,pred] = max(prob,[],2);
acc = sum(pred == testlabels) ./ numel(testlabels);    %# accuracy
fprintf('Final accuracy = %f %\n', acc*100);

我的准确度始终高于80％（~83％），每次迭代的预测标签总和几乎为零，最终精度为11％。如下图所示：

Accuracy = 65% (39/60) (classification)
sum(predicted_label==1) = 17 
Accuracy = 83.3333% (50/60) (classification)
sum(predicted_label==1) = 0 
Accuracy = 83.3333% (50/60) (classification)
sum(predicted_label==1) = 0 
Accuracy = 63.3333% (38/60) (classification)
sum(predicted_label==1) = 16 
Accuracy = 83.3333% (50/60) (classification)
sum(predicted_label==1) = 0 
Accuracy = 83.3333% (50/60) (classification)
sum(predicted_label==1) = 0 
Final accuracy = 11.666667 >>

这是怎么回事？我在这里做错了什么???

Answer 1

在我看来，你的代码很好。您获得低精度的原因是svm在此数据集上不能很好地工作。

正如你所看到的，在几个模型中，你的svm只是预测一切都是消极的。这意味着它对预测正面标签没有信心。因此，当你最终将所有概率结合起来时，准确性就会很差。

我建议你用svm尝试另一个内核来查看结果。

libsvm的准确性是不对的

1 个答案: