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Author Topic: [SOLVED] Vote operator. How it works?  (Read 273 times)
tobix10
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Posts: 7


« on: May 24, 2013, 01:41:37 PM »

Hello, I have a process where inside vote I split my data set into 5 parts with different attributes and each part goes to SVM classifier. These 5 output models are outputs of vote operator.
Everything is done in X-Validation and testing subprocess contains Apply Model -> Performance(typical approach).

I've read description of Vote Operator and it says that it uses majority vote to take prediction. My process is classification problem(only 2 classes: true, false).
I am wondering how vote exactly works.
1. Does it take for each record only predicted class from SVMs outputs and count how many times it was true and false, and take the largest one(ex. 4 SVM predicted true class for the record so vote also predicts true)?
or
2. It uses some kind of probability approach. I mean that SVM classifiers gives an information about how likely record is true and how likely it is false and vote learns on that predictions.

If first is correct how can I perform second approach ?
« Last Edit: May 29, 2013, 11:23:40 AM by Marius » Logged
earmijo
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« Reply #1 on: May 24, 2013, 05:45:00 PM »

RapidMiner uses approach 1 (it makes the different algorithms vote and assigns the observation to the class that got more votes).

If you want to average the probabilities you can use the Vote Operator available from Weka. With this one you have the choice (simple voting or averaging probabilities)
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tobix10
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« Reply #2 on: May 24, 2013, 08:16:01 PM »

Ok, thx for reply.
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