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I have a dataset with label noise which I wan't to clean with majority/consensus vote filtering. This will mean I will divide the data in K-Folds and train an ensemble model. Than using the predictions on the data I will remove rows, which are missclassified by most (majority voting) or all (consensus voting).

I have a few questions on which I can't find the answers elsewhere:

  • how to decide what models to use in the ensemble

  • the dataset is very imbalanced. Do I need to do upsampling in the majority voting?

  • do I do hyperparameter tuning in the different models, or just use standard settings?

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I have a few questions on which I can't find the answers elsewhere

It's probably because there is no simple answer to these three questions :)

I doubt there's any state of the art approach, in such cases I simply try to determine the answer to these questions empirically. Basically I create a list of hyper-parameters including the type of algorithm, the algorithm-specific hyper-parameters and any other potentially relevant option. The goal is to determine the optimal combination of values for the set of parameters. If practical I run all the combinations and select the best one. If not practical, I use a simple genetic algorithm to find an optimal combination. Of course it's suitable only if you have a dataset large enough and if the training/testing process is not too computer-intensive. You also need to be very careful about overfitting by using cross-validation and re-sampling.

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  • $\begingroup$ Dear Erwan thanks a lot for your reply. The problem us I try to clean label noise with the majority voting. How would I evaluate the perfroamnce of the different set ups? $\endgroup$ – Jeuszt Jul 7 '19 at 17:41
  • $\begingroup$ Oh right, I forgot that it's label noise so you can't evaluate against your labels. I think the only "clean" way is to annotate manually a small validation set as noise/not noise, i.e. make it a proper binary classification task that you can evaluate. if there is a massive imbalance between noise/not noise, you could bootstrap some minority class instances using a simple stage of majority voting. Any other option I can think of would amount to evaluating against the thing that you're trying to predict so it wouldn't be great. $\endgroup$ – Erwan Jul 7 '19 at 19:46
  • $\begingroup$ Thanks for your reply :) $\endgroup$ – Jeuszt Jul 8 '19 at 10:51

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