Oversampling Tabular Data with Deep Generative Models: Is it worth the effort?
In practice, machine learning experts are often confronted with imbalanced data. Without accounting for the imbalance, common classifiers perform poorly, and standard evaluation metrics mislead the practitioners on the model's performance. A standard method to treat imbalanced datasets is under- and oversampling. In this process, samples are removed from the majority class, or synthetic samples are added to the minority class. In this paper, we follow up on recent developments in deep learning. We take proposals of deep generative models and study these approaches' ability to provide realistic samples that improve performance on imbalanced classification tasks via oversampling. Across 160K+ experiments, we show that the improvements in terms of performance metric, while shown to be significant when ranking the methods like in the literature, often are minor in absolute terms, especially compared to the required effort. Furthermore, we notice that a large part of the improvement is due to undersampling, not oversampling.
Code (0)
등록된 구현이 없습니다.
Tasks
imbalanced classificationSimilar Papers 제목 키워드 기반
SOS: Score-based Oversampling for Tabular Data
Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs…
Style TransferMinority Class Oversampling for Tabular Data with Deep Generative Models
In practice, machine learning experts are often confronted with imbalanced data. Without accounting for the imbalance, common classifiers perform poorly and standard evaluation metrics mislead the practitioners on the mo…
imbalanced classificationConditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models. Popular countermeasures include oversampling the minority class. Standard methods like SMOTE rel…
ClassificationGeneral ClassificationSEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data
In the field of data mining and machine learning, commonly used classification models cannot effectively learn in unbalanced data. In order to balance the data distribution before model training, oversampling methods are…
DenoisingConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets
Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generat…
BenchmarkingFraud DetectionImage Generationimbalanced classification+2