Copertina

Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sehgal, Karan, Bhatti, Khawar Naveed
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

An Analytical Model for Overparameterized Learning Under Class Imbalance
di: Mor, Eliav, et al.
Pubblicazione: (2025)

Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control
di: Hu, Xianghong, et al.
Pubblicazione: (2026)

Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
di: Rezvani, Salim, et al.
Pubblicazione: (2024)

Class-Imbalanced Graph Learning without Class Rebalancing
di: Liu, Zhining, et al.
Pubblicazione: (2023)

Comparing Multiclass Classification Algorithms for Financial Distress Prediction
di: Zambare, Noopur, et al.
Pubblicazione: (2023)

CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data
di: Ehab, Mohamed, et al.
Pubblicazione: (2026)

Federated Active Learning Under Extreme Non-IID and Global Class Imbalance
di: Zong, Chen-Chen, et al.
Pubblicazione: (2026)

Few-Shot Learning with Class Imbalance
di: Ochal, Mateusz, et al.
Pubblicazione: (2021)

FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning
di: Chowdhury, Sujit, et al.
Pubblicazione: (2024)

An Automated Machine Learning Framework for Surgical Suturing Action Detection under Class Imbalance
di: Zhang, Baobing, et al.
Pubblicazione: (2025)

A Machine Learning Framework for Handling Unreliable Absence Label and Class Imbalance for Marine Stinger Beaching Prediction
di: Ibenegbu, Amuche, et al.
Pubblicazione: (2025)

Multi-Class Imbalanced Learning with Support Vector Machines via Differential Evolution
di: Zhang, Zhong-Liang, et al.
Pubblicazione: (2025)

A Theoretical Analysis of the Learning Dynamics under Class Imbalance
di: Francazi, Emanuele, et al.
Pubblicazione: (2022)

Class-Imbalanced Complementary-Label Learning via Weighted Loss
di: Wei, Meng, et al.
Pubblicazione: (2022)

Class Uncertainty: A Measure to Mitigate Class Imbalance
di: Baltaci, Z. S., et al.
Pubblicazione: (2023)

Impact of Leakage on Data Harmonization in Machine Learning Pipelines in Class Imbalance Across Sites
di: Nieto, Nicolás, et al.
Pubblicazione: (2024)

Architectural Trade-offs in Small Language Models Under Compute Constraints
di: Bhatti, Shivraj Singh
Pubblicazione: (2025)

Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
di: Kaushik, Chiraag, et al.
Pubblicazione: (2024)

Handling Extreme Class Imbalance: Using GANs in Data Augmentation for Suicide Prediction
di: Visweswaraiah, Vaishnavi, et al.
Pubblicazione: (2025)

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

Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance
di: Kishanthan, Sukumar, et al.
Pubblicazione: (2025)

Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
di: Zhou, Han, et al.
Pubblicazione: (2025)

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
di: Somón, Elvin, et al.
Pubblicazione: (2026)

Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning
di: Ma, Jinge, et al.
Pubblicazione: (2026)

Taking Class Imbalance Into Account in Open Set Recognition Evaluation
di: Komorniczak, Joanna, et al.
Pubblicazione: (2024)

DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning
di: Lee, Hyuck, et al.
Pubblicazione: (2026)

Federated Learning and Class Imbalances
di: Zhu, Siqi, et al.
Pubblicazione: (2026)

Personalized Prediction By Learning Halfspace Reference Classes Under Well-Behaved Distribution
di: Huang, Jizhou, et al.
Pubblicazione: (2025)

Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
di: Racha, Suraj, et al.
Pubblicazione: (2024)

Provable Benefit of Sign Descent: A Minimal Model Under Heavy-Tailed Class Imbalance
di: Yadav, Robin, et al.
Pubblicazione: (2025)

Enhancing Dimensionality Prediction in Hybrid Metal Halides via Feature Engineering and Class-Imbalance Mitigation
di: Karabin, Mariia, et al.
Pubblicazione: (2025)

Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data
di: Younis, Sohaib, et al.
Pubblicazione: (2024)

VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification
di: Hu, Yulan, et al.
Pubblicazione: (2023)

Time-Series Contrastive Learning against False Negatives and Class Imbalance
di: Jin, Xiyuan, et al.
Pubblicazione: (2023)

Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
di: Nawaz, Ali, et al.
Pubblicazione: (2025)

On the Performance of Differentially Private Optimization with Heavy-Tail Class Imbalance
di: Tang, Qiaoyue, et al.
Pubblicazione: (2025)

Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference
di: Lou, Yujia, et al.
Pubblicazione: (2025)

Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts
di: Datta, Shounak, et al.
Pubblicazione: (2017)

Understanding Imbalanced Forgetting in Rehearsal-Based Class-Incremental Learning
di: Tamajo, Alberto, et al.
Pubblicazione: (2026)

On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning
di: Guleria, Arpit, et al.
Pubblicazione: (2024)