Questions tagged [imbalanced-data]

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What is the best practice to normalize/standardize imbalanced data for outlier detection or binary classification task?

I'm researching Anomaly/outlier/fraud detection, and I'm looking for the best practice to pre-process the synthetic data for imbalanced data. I have checked all methodology for normalizing/...
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1answer
20 views

On what threshold we should resample the data?

When working with churn datasets, we usually find imbalanced datasets. My question is how to decide on what basis we should resample the data. For example: while splitting the data before training we ...
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0answers
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Is balancing class data for imbalanced problems helpful or just folklore when considering thresholds?

Caveat: I'm aware that imbalanced data questions are a dead horse, but I haven't found an answer to this flavor of it directly. When working with highly imbalanced data (e.g. binary class cases), ...
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1answer
21 views

Can the attention mask hold values between 0 and 1?

I am new to attention-based models and wanted to understand more about the attention mask in NLP models. attention_mask: an optional torch.LongTensor of shape [...
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0answers
17 views

Dealing with high frequency tokens during masked Language modelling?

Suppose I am working with a Masked Language Model to pre-train on a specific dataset. In that dataset, most sequences have a particular token of a high frequency ...
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2answers
36 views

Doesn't over(/under)sampling an imbalanced dataset cause issues?

I'm reading a lot about how to use different metrics specifically for imbalanced datasets (e.g. two classes present, but 80% of the data is one class) and how to tackle the issue of imbalanced ...
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2answers
36 views

Do we have any method which handles the imbalanced classification with sample weighting instead of class weighting?

I am looking for methods that use sample weighting instead of class weighting for the imbalanced classification. I think sample weighting is more precise than weighting all the samples from one class ...
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0answers
18 views

How to tune the proportion of minority class after oversampling in imbalanced data with cross-validation? [duplicate]

I have an imbalanced data set and I want to balance them with SMOTENC with cross-validation. In order to determine the performance of the classifier on the original data, I will cross-validation as ...
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0answers
37 views

How to apply variational autoencoder for oversampling with cross-validation?

Currently, I have an imbalanced data set with proportions 84% and 16%. I wanna use VAE as oversampling method and I want to determine the best proportions of data that results in better metrics. Also, ...
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1answer
264 views

Categorization of approaches to deal with imbalanced classes

What is the best way to categorize the approaches which have been developed to deal with imbalance class problem? This article categorizes them into: Preprocessing: includes oversampling, ...