SOLVED
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In WEKA, MLP classifier automatically ignore the missing value, but i don't know how it works.
I've read this
http://weka.8497.n7.nabble.com/How-does-MultilayerPerceptron-classifier-handle-the-missing-value-tt44918.html , it says "missing
values are assumed to be 0 in MultilayerPerceptron", so does that mean if there missing values, it will be replaced by 0 value ?
but when I replace missing values with 0 value, the result instead give reduced accuracy in WEKA.
here my practice:
I have dataset that preprocessed by NominalToBinary filter:
then I use MLP classifier, the WEKA give me 64.2857 % accuracy:
<div>=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 9 64.2857 %
Incorrectly Classified Instances 5 35.7143 %
Kappa statistic 0
Mean absolute error 0.4762
Root mean squared error 0.4934
Relative absolute error 100 %
Root relative squared error 100 %
Total Number of Instances 14
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.000 0.000 ? 0.000 ? ? 0.178 0.318 no
1.000 1.000 0.643 1.000 0.783 ? 0.178 0.555 yes
Weighted Avg. 0.643 0.643 ? 0.643 ? ? 0.178 0.470
=== Confusion Matrix ===
a b <-- classified as
0 5 | a = no
0 9 | b = yes<br></div><div></div>then I replaced missing values with 0 value:
I do MLP classifier again, then WEKA give me 57.1429 % accuracy, lower accuracy than the dataset with missing value:
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 8 57.1429 %
Incorrectly Classified Instances 6 42.8571 %
Kappa statistic 0.0667
Mean absolute error 0.3973
Root mean squared error 0.5731
Relative absolute error 83.4356 %
Root relative squared error 116.169 %
Total Number of Instances 14
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.400 0.333 0.400 0.400 0.400 0.067 0.667 0.481 no
0.667 0.600 0.667 0.667 0.667 0.067 0.667 0.850 yes
Weighted Avg. 0.571 0.505 0.571 0.571 0.571 0.067 0.667 0.718
=== Confusion Matrix ===
a b <-- classified as
2 3 | a = no
3 6 | b = yes<br>
so i don't think "Ignore missing value" same as replace the missing value with 0 value. So can you explain to me how "Ignore missing value" actually work in MLP classifier ? and how apply it in Neural Network operator in RapidMiner ?
