How do you perform bootstrapping and remove outliers in Weka?

I am just starting to play around with the Weka API and a couple of the example data sets, but just wanted to understand a couple bits and pieces. Does anyone know how to perform 0.632 bootstrapping in 开发者_如何学GoWeka?

Also how do would I go about detecting outliers (I understand there are many different methods of doing this...)?

Also how would I remove say 10% of outliers, once they have been identified?

Any help would be greatly appreciated!

Cheers,

Neil

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You can perform supervised resampling, which is what bootstrap is, using the Resample filter.