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Create a meta-estimator using BalanceCascade sampler #328

@glemaitre

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@glemaitre
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As the BalancedBaggingEnsemble, it could be possible to create a meta-estimator using the BalanceEnsemble sampler.

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pythonmite

pythonmite commented on Dec 2, 2019

@pythonmite

I can work on this issue

chkoar

chkoar commented on Dec 2, 2019

@chkoar
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@pythonmite go ahead

pythonmite

pythonmite commented on Dec 16, 2019

@pythonmite

@glemaitre I was trying to figure out the issue in order to implement. Do we need an meta-estimator which would take undersampling or oversampling methods to provide an balanced class sets.

solegalli

solegalli commented on Jan 18, 2021

@solegalli
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Hello, by any chance is the algorithm in this article the BalanceCascade in question?

  1. make a bag with random undersampling from original dataset
  2. train adaboost in the bag
  3. remove from original dataset all observations from majority class correctly classified by the adaboost
  4. repeat 1-3 with the new "original dataset"

I could try and give it a try.

But it would not be in parallel like the BalancedBagging, am I correct? because estimators are built sequentially.

Also, by BalanceCascade sampler do you mean the BalanceCascade algorithm or something else? and what do you mean by BalanceEnsemble sampler?

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          Create a meta-estimator using BalanceCascade sampler · Issue #328 · scikit-learn-contrib/imbalanced-learn