Copertina

SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regression

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Alahyari, Shayan, Domaratzki, Mike
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Local distribution-based adaptive oversampling for imbalanced regression
di: Alahyari, Shayan, et al.
Pubblicazione: (2025)

Regression Augmentation With Data-Driven Segmentation
di: Alahyari, Shayan, et al.
Pubblicazione: (2025)

More Data or Better Algorithms: Latent Diffusion Augmentation for Deep Imbalanced Regression
di: Alahyari, Shayan
Pubblicazione: (2025)

A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)
di: Mohanty, Nishikanta, et al.
Pubblicazione: (2024)

Simplicial SMOTE: Oversampling Solution to the Imbalanced Learning Problem
di: Kachan, Oleg, et al.
Pubblicazione: (2025)

Enhancing Machine Learning for Imbalanced Medical Data: A Quantum-Inspired Approach to Synthetic Oversampling (QI-SMOTE)
di: Kashtriya, Vikas, et al.
Pubblicazione: (2025)

AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification
di: Kishanthan, Sukumar, et al.
Pubblicazione: (2025)

Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design
di: Chen, Lianghong, et al.
Pubblicazione: (2025)

A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
di: Karaman, Ismail Hakki, et al.
Pubblicazione: (2024)

HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications
di: Zhao, Ziming, et al.
Pubblicazione: (2024)

Imbalance in Regression Datasets
di: Kowatsch, Daniel, et al.
Pubblicazione: (2024)

Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling
di: Giusti, Claudio, et al.
Pubblicazione: (2025)

On Imbalanced Regression with Hoeffding Trees
di: Alchirch, Pantia-Marina, et al.
Pubblicazione: (2026)

Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts
di: Maxson, Taylor, et al.
Pubblicazione: (2026)

PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
di: Zhang, Jiayu, et al.
Pubblicazione: (2025)

AEMLO: AutoEncoder-Guided Multi-Label Oversampling
di: Zhou, Ao, et al.
Pubblicazione: (2024)

Highly Imbalanced Regression with Tabular Data in SEP and Other Applications
di: Moukpe, Josias K., et al.
Pubblicazione: (2025)

Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
di: Chen, Donghe, et al.
Pubblicazione: (2025)

Adaptive Cluster-Based Synthetic Minority Oversampling Technique for Traffic Mode Choice Prediction with Imbalanced Dataset
di: Ooi, Guang An, et al.
Pubblicazione: (2025)

Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
di: Panagiotou, Emmanouil, et al.
Pubblicazione: (2024)

Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data
di: Nguyen, Hung, et al.
Pubblicazione: (2024)

High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework
di: Masi, Giuseppe, et al.
Pubblicazione: (2026)

Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method
di: Liang, Xinyu, et al.
Pubblicazione: (2024)

Uncertainty-Aware Generative Oversampling Using an Entropy-Guided Conditional Variational Autoencoder
di: Zare, Amirhossein, et al.
Pubblicazione: (2025)

Post Hoc Regression Refinement via Pairwise Rankings
di: Wijaya, Kevin Tirta, et al.
Pubblicazione: (2025)

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
di: Ramachandranpillai, Resmi, et al.
Pubblicazione: (2024)

Addressing Multilabel Imbalance with an Efficiency-Focused Approach Using Diffusion Model-Generated Synthetic Samples
di: Charte, Francisco, et al.
Pubblicazione: (2025)

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning
di: Gittlin, Hunter
Pubblicazione: (2025)

Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
di: Lomurno, Eugenio, et al.
Pubblicazione: (2026)

AIS-CycleGen: A CycleGAN-Based Framework for High-Fidelity Synthetic AIS Data Generation and Augmentation
di: Zaman, SM Ashfaq uz, et al.
Pubblicazione: (2026)

Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification
di: Pei, Wenbin, et al.
Pubblicazione: (2026)

QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation
di: Zheng, Kangyu, et al.
Pubblicazione: (2025)

$α$-GAN by Rényi Cross Entropy
di: Ding, Ni, et al.
Pubblicazione: (2025)

Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers
di: Hamidi, Shayan Mohajer, et al.
Pubblicazione: (2024)

GAN-SLAM: Real-Time GAN Aided Floor Plan Creation Through SLAM
di: Davies, Leon, et al.
Pubblicazione: (2025)

Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
di: Nakada, Ryumei, et al.
Pubblicazione: (2024)

Geometric Manifold Rectification for Imbalanced Learning
di: Wang, Xubin, et al.
Pubblicazione: (2026)

Offline Reinforcement Learning with Imbalanced Datasets
di: Jiang, Li, et al.
Pubblicazione: (2023)

AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
di: Hamidi, Shayan Mohajer, et al.
Pubblicazione: (2024)

Conformal Prediction with Learned Features
di: Kiyani, Shayan, et al.
Pubblicazione: (2024)