weka.classifiers.misc.HyperPipes

weka.classifiers.misc.HyperPipes 屬類別屬性區間學習器，
記錄符合類別的屬性出現區間，再預測屬性符合比例高之類別，可提供案例集基本表現值供標竿比較之用。

HyperPipes 學習分類時，為每個類別建立一個超區間(hyperpipe)，記錄每個屬性有出現該類別的案例區間為何。
預測分類時，計算新案例符合各類別的超區間程度，取符合程度高者為其預測類別。
案例符合某類別超區間程度(0~1)乃案例有多少比例(介於0~100%)的屬性落於某類別超區間的屬性描述區間內。

> java -cp weka.jar;. weka.classifiers.misc.HyperPipes  -t data\weather.numeric.arff

HyperPipes classifier
HyperPipe for class: yes
  temperature: 64.0,83.0,
  humidity: 65.0,96.0,
  outlook: true,true,true,
  windy: true,true,

HyperPipe for class: no
  temperature: 65.0,85.0,
  humidity: 70.0,95.0,
  outlook: true,false,true,
  windy: true,true,


Time taken to build model: 0 seconds
Time taken to test model on training data: 0 seconds

=== Error on training data ===

Correctly Classified Instances          10               71.4286 %
Incorrectly Classified Instances         4               28.5714 %
Kappa statistic                          0.2432
Mean absolute error                      0.4531
Root mean squared error                  0.4597
Relative absolute error                 97.5824 %
Root relative squared error             95.8699 %
Total Number of Instances               14


=== Confusion Matrix ===

 a b   <-- classified as
 9 0 | a = yes
 4 1 | b = no



=== Stratified cross-validation ===

Correctly Classified Instances           9               64.2857 %
Incorrectly Classified Instances         5               35.7143 %
Kappa statistic                          0
Mean absolute error                      0.483
Root mean squared error                  0.4899
Relative absolute error                101.4286 %
Root relative squared error             99.3055 %
Total Number of Instances               14


=== Confusion Matrix ===

 a b   <-- classified as
 9 0 | a = yes
 5 0 | b = no


如下 weather.numeric.arff 案例集的14個案例有9個yes，5個no。

 
 
 
 
 
 


  
outlook
  
temperature
  
humidity
  
windy
  
play
 

  
sunny
  
85
  
85
  
FALSE
  
no
 

  
sunny
  
80
  
90
  
TRUE
  
no
 

  
rainy
  
65
  
70
  
TRUE
  
no
 

  
sunny
  
72
  
95
  
FALSE
  
no
 

  
rainy
  
71
  
91
  
TRUE
  
no
 

  
overcast
  
83
  
86
  
FALSE
  
yes
 

  
rainy
  
70
  
96
  
FALSE
  
yes
 

  
rainy
  
68
  
80
  
FALSE
  
yes
 

  
overcast
  
64
  
65
  
TRUE
  
yes
 

  
sunny
  
69
  
70
  
FALSE
  
yes
 

  
rainy
  
75
  
80
  
FALSE
  
yes
 

  
sunny
  
75
  
70
  
TRUE
  
yes
 

  
overcast
  
72
  
90
  
TRUE
  
yes
 

  
overcast
  
81
  
75
  
FALSE
  
yes
 


參考: weka.classifiers.misc.HyperPipes
1. source code
2. documentation