how does

rieqyerysya

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 thishttp://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:


然后I use MLP classifier, the WEKA give me 64.2857 % accuracy:

=== 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

然后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

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 ?

varunm1

Hello,

Did you resolve the issue? I see you kept SOLVED in the question, if not can you share your XML process (View --> Show Panel --> XML).

Thanks