Questions tagged [imbalanced-datasets]
For questions that involve imbalanced (or unbalanced) datasets.
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What's the best way to train data with unbalanced targets?
Suppose I have data I want to use for supervised learning, but there is a pretty bad target/class/labels imbalance. Should I:
Limit the size of the training set to make sure there is a flat target/...
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1answer
29 views
How to arrange test dataset distribution for an imbalanced classification problem?
I have a dataset that contains 560 datapoints, and I would like to do binary classification on it. 400 datapoints belong to class 1, and 160 points belong to class 2. In the case of an imbalanced ...
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1answer
32 views
How to take data at regular intervals? [closed]
The attached image is of a raw dataset from where I need the data of highlighted column after each interval of 10 rows. So, how can I write code for this?
2
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1answer
33 views
How do you handle unbalanced image datasets?
I have an image data set on which I am training a CNN. The data set is slightly unbalanced. So, my solution up till now was to delete some images of the majority class.
But I now realize that there ...
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1answer
37 views
How to handle an unbalanced dataset when training object detection algorithms?
I am training an object detection model, and I have some very highly unbalanced data annotations. I have almost 11,000 images, all with dimensions of 1024 $\times$ 1024.
Within those images I have the ...
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12 views
How to deal with unbalanced data in multilabel classification problem
I have 3 possible solutions, but I am not sure if they are good. I think they are a bit clunky (especially 1st and 2nd).
Use multiple small models. So instead of having the model that can tell you ...
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0answers
5 views
Evaluating a CNN -multi class model with two separate thresholds
I have a model that outputs three classes. But here instead of one threshold, it depends on a combination of two (user input threshold). One threshold varies from 0.1 to 1.0 and the other varies from ...
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8 views
Evaluating a convolutional neural network on an imbalanced (academic) dataset
I have trained a posture analysis network to classify in a video of humans recorded in public places if there is a) shake-hand between two humans, b) Standing close together that their hands touch ...
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24 views
Doesn't imbalanced data affect input variables too?
I was thinking today about how biased data affects machine learning performance, and I begin to wonder why class imbalance (or data imbalance in general) is only talked about in a classification ...
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36 views
Why am I not getting a good accuracy but bad precision and recall for this binary classification problem (with an unbalanced dataset)?
I'm working on a simple MLP classification problem where I need to have the output layer as softmax.
The dataset that I've been using needs to pass through a parse that I made to remove NaN and change ...
2
votes
1answer
36 views
How to handle class imbalancing when the actual data are that way
My supervised learning training data are obtained from actual data; and in real cases, there's one class which happens less often than other classes, just around 5% of all cases.
To be precise, the ...
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vote
1answer
43 views
Handling imbalanced data with multiple targets
I have the model which has 3 outputs (it is a regression task, I have the angle of the steering wheel, brake and acceleration). I can divide my values to some smaller bins and in this way I can change ...
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1answer
41 views
Multi class text classification when having only one sample for classes
I have a dataset of texts, each text was identified with an ID number. I would like to do a prediction by finding the best match ID number for upcoming new texts. To use multi text classification, I ...
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1answer
37 views
What does "adding class weights for an imbalanced dataset" mean in the case of multi-label classification?
Suppose I have the following toy data set:
Each instance has multiple labels at a time.
You can see I have 2 instances for Label2. However, only one instance for the other labels. It means that we ...
3
votes
1answer
79 views
How do I select the (number of) negative cases, if I'm given a set of positive cases?
We were given a list of labeled data (around 100) of known positive cases, i.e. people that have a certain disease, i.e. all these people are labeled with the same class (disease). We also have a much ...
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17 views
Is the PR AUC invariant under label flip?
The ROC-AUC curve is invariant under a flip of the labels. I don't know if it's a famous result, so I will give the proof below. My question is if the PR-AUC curve also has this property. I have not ...
3
votes
1answer
105 views
How robust are deep networks to class imbalance?
Before deep learning, I worked with machine learning problems where the data had a large class imbalance (30:1 or worse ratios). At that time, all the classifiers struggled, even after under-sampling ...
3
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1answer
71 views
How to perform binary classification when one class is more predominant than the other?
Assuming we have big $m \times n$ input dataset, with $m \times 1$ output vector. It's a classification problem with only two possible values: either $1$ or $0$.
Now, the problem is that almost all ...
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1answer
123 views
Is it possible to combine k-fold cross-validation and oversampling for a multi-class text classification task with imbalanced data?
I am dealing with an intent classification task on an Italian customer service data set.
I've more or less 1.5k sentences and 29 classes (imbalanced).
According to the literature, a good choice is to ...
4
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1answer
100 views
How can I use Generative Adversarial Networks to solve the imbalanced class problem?
Problem setting
We have to do a binary classification of data given a training dataset $D$, where most items belong to class $A$ and some items belong to class $B$, so the classes are heavily ...
