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## 개요
> Imbalanced Data Problem
## idea
> 나이 분포를 보면 데이터가 불균형하여 학습이 잘 이뤄지지 않을 수 있음
> 따라서 RandAugment를 하지 않고 오프라인으로 각 나이에 대한 Augmentation을 하여 나이에 대한 데이터의 균형을 맞춰준다.
## location
> bone_d…
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Hi, I want to use the `PR-AUC` (or `ROC-AUC`) metrics for a few-shot classification problem where the test data is imbalanced.
Therefore, I need the positive (yes) **probability** to calculate this…
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## 論文リンク
https://arxiv.org/abs/1911.02855
## 公開日(yyyy/mm/dd)
2019/11/07
## 概要
単純な cross entropy では、 accuracy のみを見て False Positive, False Negative 両方をケアできてない点、簡単なデータが大量にあったときにそれらの寄与が dominant …
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Hi arnoweng,
I have two following questions in this project.
1):
I'm wondering why to apply tencrop technique on testing images. I thought data augmentation techniques should only be applied on t…
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I'm new to TFP and probabilistic models. With deterministic NNs, I could balance my data by oversampling. However, my intuition says one shouldn't do this with probabilistic networks.
Currently, I'…
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# Tweet summary
Train multiple classifiers with bootstrap undersample data set on imbalanced data.
# Useful link
https://www.svds.com/learning-imbalanced-classes/#fn2
https://imbalanced-learn.or…
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Add [SMOTE|http://www.jair.org/papers/paper953.html], "Synthetic Minority Over-sampling Technique" for handling imbalanced datasets/ This is a more sophisticated means of balancing the dataset vs str…
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Hi,
I have a multi-label dataset with extremely imbalanced classes, are there any developed methods in scikit-multilearn working well on this imbalanced data? thanks.
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