使用LIBSVM的多类 [英] MultiClass using LIBSVM

问题描述

我有一个多类svm分类（6类）。我想使用LIBSVM对它进行分类。以下是我试过的，我有一些问题，他们。

Method1（一个对一个）：

  model = svmtrain TrainLabel，TrainVec，'-c 1 -g 0.00154 -b 0.9'）; 
 [predict_label，accuracy，dec_values] = svmpredict（TestLabel，TestVec，model）; 
  

有关此方法的两个问题：1）是我需要做的多类问题
2）对于n in'-b n'应该有什么值。我不确定

方法2（一个vs休息）：

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

对于第二种方法，如何附加分类分数。我不能投票。

此外，这是我尝试的两种方法。哪种方法更好？

想听听一些评论。

解决方案

关于'-b'参数，在LIBSVM自述文件中， p>

-b probability_estimates：是否为概率估计训练SVC或SVR模型，0或1（默认为0）

因此，如果你想要训练的模型返回类概率，你应该指定'-b 1';如果你没有返回类概率，你应该指定'-b 0'。您只需调用 svmtrain 一次。此外，如果为训练指定-b 1，则还必须指定它进行预测。

I have a multiclass svm classification(6 class). I would like to classify it using LIBSVM. The following are the ones that i have tried and i have some questions regarding them.

Method1( one vs one):

model = svmtrain(TrainLabel, TrainVec, '-c 1 -g 0.00154 -b 0.9');
[predict_label, accuracy, dec_values] = svmpredict(TestLabel, TestVec, model);

Two questions about this method: 1) is that all i need to do for multiclass problem 2) what value should it be for n in '-b n'. I m not sure

Method 2( one vs rest):

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

For the second method,how do I attach classification scores. I am not able to do voting.

Besides that,these are the two methods I have tried. Which method is better?

Would like to hear some comments. Please correct me if I am wrong.

解决方案

Regarding the '-b' parameter, in the LIBSVM README it says:

-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)

Therefore, you should specify '-b 1' if you want the trained model to return class probabilities, and '-b 0' if you don't. You only need to call svmtrain once. Also, if you specify '-b 1' for training, you must also specify it for prediction.

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