Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
Fuente:
arXiv
Saved in:
| Main Authors: | Sakho, Abdoulaye, Malherbe, Emmanuel, Gauthier, Carl-Erik, Scornet, Erwan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
by: Sakho, Abdoulaye, et al.
Published: (2024)
by: Sakho, Abdoulaye, et al.
Published: (2024)
Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
by: Mayala, Moria, et al.
Published: (2025)
by: Mayala, Moria, et al.
Published: (2025)
Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
by: Genans, Ferdinand, et al.
Published: (2026)
by: Genans, Ferdinand, et al.
Published: (2026)
Privacy Amplification by Missing Data
by: Roburin, Simon, et al.
Published: (2026)
by: Roburin, Simon, et al.
Published: (2026)
Principled Federated Random Forests for Heterogeneous Data
by: Khellaf, Rémi, et al.
Published: (2026)
by: Khellaf, Rémi, et al.
Published: (2026)
When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
by: Muller, Christophe, et al.
Published: (2025)
by: Muller, Christophe, et al.
Published: (2025)
Beyond Imbalance Ratio: Data Characteristics as Critical Moderators of Oversampling Method Selection
by: Jiang, Yuwen, et al.
Published: (2026)
by: Jiang, Yuwen, et al.
Published: (2026)
Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
by: Li, Wenjie, et al.
Published: (2025)
by: Li, Wenjie, et al.
Published: (2025)
GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification
by: Rustamov, Zahiriddin, et al.
Published: (2024)
by: Rustamov, Zahiriddin, et al.
Published: (2024)
Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
by: Nakada, Ryumei, et al.
Published: (2024)
by: Nakada, Ryumei, et al.
Published: (2024)
Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification
by: Avela, Aleksi, et al.
Published: (2025)
by: Avela, Aleksi, et al.
Published: (2025)
A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
by: Karaman, Ismail Hakki, et al.
Published: (2024)
by: Karaman, Ismail Hakki, et al.
Published: (2024)
Simplicial SMOTE: Oversampling Solution to the Imbalanced Learning Problem
by: Kachan, Oleg, et al.
Published: (2025)
by: Kachan, Oleg, et al.
Published: (2025)
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
by: Ma, Rongrong, et al.
Published: (2024)
by: Ma, Rongrong, et al.
Published: (2024)
AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification
by: Kishanthan, Sukumar, et al.
Published: (2025)
by: Kishanthan, Sukumar, et al.
Published: (2025)
SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regression
by: Alahyari, Shayan, et al.
Published: (2025)
by: Alahyari, Shayan, et al.
Published: (2025)
Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification
by: Kishanthan, Sukumar, et al.
Published: (2025)
by: Kishanthan, Sukumar, et al.
Published: (2025)
Random features models: a way to study the success of naive imputation
by: Ayme, Alexis, et al.
Published: (2024)
by: Ayme, Alexis, et al.
Published: (2024)
HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications
by: Zhao, Ziming, et al.
Published: (2024)
by: Zhao, Ziming, et al.
Published: (2024)
Enhancing Machine Learning for Imbalanced Medical Data: A Quantum-Inspired Approach to Synthetic Oversampling (QI-SMOTE)
by: Kashtriya, Vikas, et al.
Published: (2025)
by: Kashtriya, Vikas, et al.
Published: (2025)
Adaptive Cluster-Based Synthetic Minority Oversampling Technique for Traffic Mode Choice Prediction with Imbalanced Dataset
by: Ooi, Guang An, et al.
Published: (2025)
by: Ooi, Guang An, et al.
Published: (2025)
Enhancing Synthetic Oversampling for Imbalanced Datasets Using Proxima-Orion Neighbors and q-Gaussian Weighting Technique
by: Yadav, Pankaj, et al.
Published: (2025)
by: Yadav, Pankaj, et al.
Published: (2025)
On the consistency of supervised learning with missing values
by: Josse, Julie, et al.
Published: (2019)
by: Josse, Julie, et al.
Published: (2019)
An Oversampling-enhanced Multi-class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-reported Outcomes
by: Yan, Yang, et al.
Published: (2024)
by: Yan, Yang, et al.
Published: (2024)
Predicting and Explaining Customer Data Sharing in the Open Banking
by: de Brito, João B. G., et al.
Published: (2025)
by: de Brito, João B. G., et al.
Published: (2025)
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
by: Ramsey, Joseph D.
Published: (2026)
by: Ramsey, Joseph D.
Published: (2026)
Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data
by: Khan, Zardad, et al.
Published: (2024)
by: Khan, Zardad, et al.
Published: (2024)
Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification
by: Pei, Wenbin, et al.
Published: (2026)
by: Pei, Wenbin, et al.
Published: (2026)
Oversampling and Downsampling with Core-Boundary Awareness: A Data Quality-Driven Approach
by: Belhaouari, Samir Brahim, et al.
Published: (2025)
by: Belhaouari, Samir Brahim, et al.
Published: (2025)
Topological Data Analysis for Unsupervised Anomaly Detection and Customer Segmentation on Banking Data
by: Barberi, Leonardo Aldo Alejandro, et al.
Published: (2025)
by: Barberi, Leonardo Aldo Alejandro, et al.
Published: (2025)
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
by: Shilova, Veronika, et al.
Published: (2025)
by: Shilova, Veronika, et al.
Published: (2025)
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data
by: Dablain, Damien A., et al.
Published: (2024)
by: Dablain, Damien A., et al.
Published: (2024)
OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness
by: Barnard, Amanda S
Published: (2026)
by: Barnard, Amanda S
Published: (2026)
Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses
by: Remita, Amine M., et al.
Published: (2019)
by: Remita, Amine M., et al.
Published: (2019)
Oversampling techniques for predicting COVID-19 patient length of stay
by: Farahany, Zachariah, et al.
Published: (2025)
by: Farahany, Zachariah, et al.
Published: (2025)
Highly Imbalanced Regression with Tabular Data in SEP and Other Applications
by: Moukpe, Josias K., et al.
Published: (2025)
by: Moukpe, Josias K., et al.
Published: (2025)
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
by: Kaushik, Chiraag, et al.
Published: (2024)
by: Kaushik, Chiraag, et al.
Published: (2024)
AEMLO: AutoEncoder-Guided Multi-Label Oversampling
by: Zhou, Ao, et al.
Published: (2024)
by: Zhou, Ao, et al.
Published: (2024)
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
by: Zhang, Hengrui, et al.
Published: (2023)
by: Zhang, Hengrui, et al.
Published: (2023)
Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling
by: Emaan, Kashaf Ul
Published: (2025)
by: Emaan, Kashaf Ul
Published: (2025)
Similar Items
-
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
by: Sakho, Abdoulaye, et al.
Published: (2024) -
Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
by: Mayala, Moria, et al.
Published: (2025) -
Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
by: Genans, Ferdinand, et al.
Published: (2026) -
Privacy Amplification by Missing Data
by: Roburin, Simon, et al.
Published: (2026) -
Principled Federated Random Forests for Heterogeneous Data
by: Khellaf, Rémi, et al.
Published: (2026)