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Classification on large datasets

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Classification on large datasets

Postby manisha » Thu Dec 19, 2013 18:45


I want to bulid classification models from large datasets and test them on even larger datasets.
The largest data that I want to test has 1255443 instances with number of features varying between 6-31. I find the classification of test examples takes a lot of time with Decision tree, knn and naive.

Can anyone please suggest me which algorithm should I use to reduce the time of execution? I am already using a redhat linux machine with 8 processors and I am using 2.7 version of orange. I also want to use some ensemble methods for classification. Mostly I am interested in using decision tree.
Thanks for a reply

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