"Multi-class Classification using SVM"
Hi,
I want to classify data into positive, negative & neutral.
For this I want to use 2 SVM one trained on positive & not positive data & the other on negative & not negative data. Now I want to take the data predicted as positive by the 1st classifier & not negative by the second classifier as positive & data predicted as negative by the 2nd classifier & not positive by the 1st classifier as negative. In rest of the cases I want to tag the data as neutral.
Is it possible to do this in Rapid-Miner in GUI Mode?
I want to classify data into positive, negative & neutral.
For this I want to use 2 SVM one trained on positive & not positive data & the other on negative & not negative data. Now I want to take the data predicted as positive by the 1st classifier & not negative by the second classifier as positive & data predicted as negative by the 2nd classifier & not positive by the 1st classifier as negative. In rest of the cases I want to tag the data as neutral.
Is it possible to do this in Rapid-Miner in GUI Mode?
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Thanks for replying.
I am very new to rapidminer. Can u plez explain a bit more? I am not able to put the 2 classiifers under the same root process. Can you please tell how can I put the 2 classifers under the same root process?
I am very new to rapidminer. Can u plez explain a bit more? I am not able to put the 2 classiifers under the same root process. Can you please tell how can I put the 2 classifers under the same root process?
Dear D.M.,
that depends very much on how your data looks like and what you want to achieve. A general approach would be the following:
- Use an AttributeConstruction to generate "isPositiv" and "isNegative" binary attribute
- Declare the first as a label (ChangeAttributeRole)
- Train a model
- Set the second as a label
- Train another model
- Then, apply both models subsequently to the test set, and perform the inverse attribute construction on the two prediction attributes you get.
I would recommend to have a look at the RapidMiner training courses. E.g., an introductory course is this one: http://rapid-i.com/content/view/110/143/lang,en/
Best,
Simon
that depends very much on how your data looks like and what you want to achieve. A general approach would be the following:
- Use an AttributeConstruction to generate "isPositiv" and "isNegative" binary attribute
- Declare the first as a label (ChangeAttributeRole)
- Train a model
- Set the second as a label
- Train another model
- Then, apply both models subsequently to the test set, and perform the inverse attribute construction on the two prediction attributes you get.
I would recommend to have a look at the RapidMiner training courses. E.g., an introductory course is this one: http://rapid-i.com/content/view/110/143/lang,en/
Best,
Simon
please look at the AttributeConstruction operator. You can use the if(condition,thenValue,elseValue) function to construct your target attribute based on the two other predictions.
Best,
Simon