weka.classifiers.functions.VotedPerceptron

weka.classifiers.functions.VotedPerceptron 為投票型感知器，屬錯誤驅動型學習器。
先全域性取代缺值，再轉換文字屬性為二元屬性，適用於預測二元類別值，可線上累進學習。

給定案例屬性 a=(a0, a1, ..., ak)，權重向量 w=(w0, w1, ..., wk)
其中，a 屬性值為二元值 0 或 1，擴充屬性 a0 恆為 1。
預測式為
  w0 * a0 + w1 * a1 + ... + wk * ak > 0 表類別1; 否則類別2

學習過程若遇預測錯誤，則權重向量調整法如下:
  類別2誤為類別1:   w -= a  讓權重變小
  類別1誤為類別2:   w += a  讓權重變大

參數說明:
 -I  套用訓練集學習權重的輪數。預設值1
 -E  多項式核函數(polynomial kernel)之次方。預設值1
 -S  亂數種子，影響訓練集的案例訓練順序。預設值1
 -M  最大允許權重修正次數。預設值10000

> java  weka.classifiers.functions.VotedPerceptron  -t data\weather.numeric.arff


VotedPerceptron: Number of perceptrons=5


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

=== Error on training data ===

Correctly Classified Instances           9               64.2857 %
Incorrectly Classified Instances         5               35.7143 %
Kappa statistic                          0     
Mean absolute error                      0.3623
Root mean squared error                  0.587 
Relative absolute error                 78.0299 %
Root relative squared error            122.4306 %
Total Number of Instances               14     


=== Confusion Matrix ===

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



=== Stratified cross-validation ===

Correctly Classified Instances           9               64.2857 %
Incorrectly Classified Instances         5               35.7143 %
Kappa statistic                          0     
Mean absolute error                      0.3736
Root mean squared error                  0.589 
Relative absolute error                 78.4565 %
Root relative squared error            119.3809 %
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個案例利用2個文字屬性及2個數字屬性，預測文字屬性。

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:
1.weka.classifiers.functions.VotedPerceptron
   code | doc