Matlab 如何根据Weka提供的结果绘制DET曲线？

我面临4个类别之间的分类问题，我使用Weka进行分类，结果如下表所示：

Correctly Classified Instances        3860               96.5    %
Incorrectly Classified Instances       140                3.5    %
Kappa statistic                          0.9533
Mean absolute error                      0.0178
Root mean squared error                  0.1235
Relative absolute error                  4.7401 %
Root relative squared error             28.5106 %
Total Number of Instances             4000     

=== Detailed Accuracy By Class ===

               TP Rate   FP Rate   Precision   Recall  F-Measure   ROC Area  Class
                 0.98      0.022      0.936     0.98      0.957      0.998    A
                 0.92      0.009      0.973     0.92      0.946      0.997    B
                 0.991     0.006      0.982     0.991     0.987      1        C
                 0.969     0.01       0.971     0.969     0.97       0.998    D
Weighted Avg.    0.965     0.012      0.965     0.965     0.965      0.998

=== Confusion Matrix ===

   a   b   c   d   <-- classified as
 980  17   1   2 |   a = A
  61 920   1  18 |   b = B
   0   0 991   9 |   c = C
   6   9  16 969 |   d = D

函数

Compute\u DET

的代码为：

[Pmiss, Pfa] = Compute_DET(true_scores, false_scores)
num_true = max(size(true_scores));
num_false = max(size(false_scores));

total=num_true+num_false;

Pmiss = zeros(num_true+num_false+1, 1); %preallocate for speed
Pfa   = zeros(num_true+num_false+1, 1); %preallocate for speed

scores(1:num_false,1) = false_scores;
scores(1:num_false,2) = 0;
scores(num_false+1:total,1) = true_scores;
scores(num_false+1:total,2) = 1;

scores=DETsort(scores);

sumtrue=cumsum(scores(:,2),1);
sumfalse=num_false - ([1:total]'-sumtrue);

Pmiss(1) = 0;
Pfa(1) = 1.0;
Pmiss(2:total+1) = sumtrue  ./ num_true;
Pfa(2:total+1)   = sumfalse ./ num_false;

return

---

但是我在翻译不同参数的含义时遇到了一个问题。例如，

mean_False

和

stdv_False

的意义是什么？与Weka参数的对应关系是什么？

为什么标题中都有大写字母？我认为您使用的是NIST DETware。涉及mean_False和stdv_False的代码片段正在合成随机示例数据以供演示（因此，这些是负面示例分数的平均值和标准偏差，当正面分数为零平均值和单位SD时）。您需要为所有测试样本计算分类器的实际分数（预阈值），然后调用Compute_DET（分数（A_示例）、分数（[B_示例、C_示例、D_示例]）来计算A的DET曲线，等等，其中A_示例是真实类为A的示例的索引向量，等等。

[Pmiss, Pfa] = Compute_DET(true_scores, false_scores)
num_true = max(size(true_scores));
num_false = max(size(false_scores));

total=num_true+num_false;

Pmiss = zeros(num_true+num_false+1, 1); %preallocate for speed
Pfa   = zeros(num_true+num_false+1, 1); %preallocate for speed

scores(1:num_false,1) = false_scores;
scores(1:num_false,2) = 0;
scores(num_false+1:total,1) = true_scores;
scores(num_false+1:total,2) = 1;

scores=DETsort(scores);

sumtrue=cumsum(scores(:,2),1);
sumfalse=num_false - ([1:total]'-sumtrue);

Pmiss(1) = 0;
Pfa(1) = 1.0;
Pmiss(2:total+1) = sumtrue  ./ num_true;
Pfa(2:total+1)   = sumfalse ./ num_false;

return