Weka中决策树和混淆矩阵中正确/错误分类实例的差异

Question

我一直在使用Weka的J48决策树将RSS提要中的关键字频率分类为目标类别。我想我可能在协调生成的决策树与报告的正确分类的实例数以及混淆矩阵中的实例数方面存在问题。

例如，我的一个. arff文件包含以下数据摘录：

@attribute Keyword_1_nasa_Frequency numeric
@attribute Keyword_2_fish_Frequency numeric
@attribute Keyword_3_kill_Frequency numeric
@attribute Keyword_4_show_Frequency numeric
...
@attribute Keyword_64_fear_Frequency numeric
@attribute RSSFeedCategoryDescription {BFE,FCL,F,M, NCA, SNT,S}

@data
0,0,0,34,0,0,0,0,0,40,0,0,0,0,0,0,0,0,0,0,24,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
0,0,0,10,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
...
20,0,64,19,0,162,0,0,36,72,179,24,24,47,24,40,0,48,0,0,0,97,24,0,48,205,143,62,78,
0,0,216,0,36,24,24,0,0,24,0,0,0,0,140,24,0,0,0,0,72,176,0,0,144,48,0,38,0,284,
221,72,0,72,0,SNT
...
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,S

以此类推：总共有64个关键字（列）和570行，其中每一行都包含一天提要中关键字的频率。在这种情况下，10天内有57条feed，总共有570条记录需要分类。每个关键字都以代理项编号作为前缀，并以“频率”作为后缀。

我对决策树的使用是使用10倍验证的默认参数。

Weka报告如下：

Correctly Classified Instances         210               36.8421 %
Incorrectly Classified Instances       360               63.1579 %

使用以下混淆矩阵：

=== Confusion Matrix ===

   a   b   c   d   e   f   g   <-- classified as
  11   0   0   0  39   0   0 |   a = BFE
   0   0   0   0  60   0   0 |   b = FCL
   1   0   5   0  72   0   2 |   c = F
   0   0   1   0  69   0   0 |   d = M
   3   0   0   0 153   0   4 |   e = NCA
   0   0   0   0  90  10   0 |   f = SNT
   0   0   0   0  19   0  31 |   g = S

树如下：

Keyword_22_health_Frequency <= 0
|   Keyword_7_open_Frequency <= 0
|   |   Keyword_52_libya_Frequency <= 0
|   |   |   Keyword_21_job_Frequency <= 0
|   |   |   |   Keyword_48_pic_Frequency <= 0
|   |   |   |   |   Keyword_63_world_Frequency <= 0
|   |   |   |   |   |   Keyword_26_day_Frequency <= 0: NCA (461.0/343.0)
|   |   |   |   |   |   Keyword_26_day_Frequency > 0: BFE (8.0/3.0)
|   |   |   |   |   Keyword_63_world_Frequency > 0
|   |   |   |   |   |   Keyword_31_gaddafi_Frequency <= 0: S (4.0/1.0)
|   |   |   |   |   |   Keyword_31_gaddafi_Frequency > 0: NCA (3.0)
|   |   |   |   Keyword_48_pic_Frequency > 0: F (7.0)
|   |   |   Keyword_21_job_Frequency > 0: BFE (10.0/1.0)
|   |   Keyword_52_libya_Frequency > 0: NCA (31.0)
|   Keyword_7_open_Frequency > 0
|   |   Keyword_31_gaddafi_Frequency <= 0: S (32.0/1.0)
|   |   Keyword_31_gaddafi_Frequency > 0: NCA (4.0)
Keyword_22_health_Frequency > 0: SNT (10.0)

我的问题是关于协调矩阵和树，反之亦然。据我所知，像（461.0/343.0)这样的评级表明461个实例被归类为NCA。但是当矩阵只显示153个时，这怎么可能呢？我不知道如何解释这一点，所以欢迎任何帮助。

提前谢谢。

Answer 1

每一页括号中的数字应读作（此页上此分类的总实例数/此页上不正确分类的数量）。

在您的第一个NCA叶的示例中，它说有461个测试实例被归类为NCA，在这461个实例中，有343个不正确的分类。因此，该叶461-343 = 118正确分类的实例。

查看决策树，请注意NCA也位于其他叶。我统计了461 3 31 4=499个NCA总分类中118 3 31 4=156个正确分类的实例。

您的混淆矩阵显示了NCA总分类中39 60 72 69 153 90 19 = 502153个正确的NCA分类。

因此，这棵树（156/499）和你的混淆矩阵（153/502）之间有一点不同。

请注意，如果您从命令行运行Weka，它会输出一个树和一个混淆矩阵，用于测试所有训练数据以及交叉验证测试。请注意，您正在为正确的树查看正确的矩阵。Weka输出训练和测试结果，从而产生两对矩阵和树。您可能混淆了它们。