Imagen de Portada

Sharp error bounds for imbalanced classification: how many examples in the minority class?

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Aghbalou, Anass, Portier, François, Sabourin, Anne
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Cross-validation on Extreme Regions
por: Aghbalou, Anass, et al.
Publicado: (2022)

Concentration and excess risk bounds for imbalanced classification with synthetic oversampling
por: Ahmad, Touqeer, et al.
Publicado: (2025)

Nearest neighbor empirical processes
por: Portier, François
Publicado: (2021)

Riemannian Stochastic Optimization for Sufficient Dimension Reduction
por: Pautrel, Thibault, et al.
Publicado: (2026)

Ultra-imbalanced classification guided by statistical information
por: Jin, Yin, et al.
Publicado: (2024)

Sharp bounds on aggregate expert error
por: Kontorovich, Aryeh, et al.
Publicado: (2024)

Robust performance metrics for imbalanced classification problems
por: Holzmann, Hajo, et al.
Publicado: (2024)

Challenges in the calibration of tree-based models for imbalanced classification
por: Phelps, Nathan, et al.
Publicado: (2024)

Adaptive kernel-density approach for imbalanced binary classification
por: Nishimura, Kotaro J., et al.
Publicado: (2025)

Sharp concentration of uniform generalization errors in binary linear classification
por: Nakakita, Shogo
Publicado: (2025)

Nearest Neighbor Sampling for Covariate Shift Adaptation
por: Portier, François, et al.
Publicado: (2023)

A statistical theory of overfitting for imbalanced classification
por: Lyu, Jingyang, et al.
Publicado: (2025)

Multi-site modelling and reconstruction of past extreme skew surges along the French Atlantic coast
por: Huet, Nathan, et al.
Publicado: (2025)

VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification
por: Wu, Hongfei, et al.
Publicado: (2026)

Extrapolation in Statistical Learning with Extreme Value Theory
por: Engelke, Sebastian, et al.
Publicado: (2026)

Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification
por: Rosu, Radu-Andrei, et al.
Publicado: (2024)

Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution
por: Alomari, Alaa, et al.
Publicado: (2024)

Analyzing decision tree bias towards the minority class
por: Phelps, Nathan, et al.
Publicado: (2025)

An extended asymmetric sigmoid with Perceptron (SIGTRON) for imbalanced linear classification
por: Woo, Hyenkyun
Publicado: (2023)

Emergent properties with repeated examples
por: Charton, François, et al.
Publicado: (2024)

Focused PU learning from imbalanced data
por: Zavitsanos, Elias, et al.
Publicado: (2026)

Sharp bounds for max-sliced Wasserstein distances
por: Boedihardjo, March T.
Publicado: (2024)

How many classes do we need to see for novel class discovery?
por: Sarkar, Akanksha, et al.
Publicado: (2025)

Learning from positive and unlabeled examples -Finite size sample bounds
por: Mansouri, Farnam, et al.
Publicado: (2025)

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

When resampling/reweighting improves feature learning in imbalanced classification?: A toy-model study
por: Obuchi, Tomoyuki, et al.
Publicado: (2024)

On Regression in Extreme Regions
por: Clémençon, Stephan, et al.
Publicado: (2023)

Uniform error bounds for quantized dynamical models
por: Metakalard, Abdelkader, et al.
Publicado: (2026)

Network Embedding with Completely-imbalanced Labels
por: Wang, Zheng, et al.
Publicado: (2020)

Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
por: Qin, Jiawen, et al.
Publicado: (2024)

Resampling strategies for imbalanced regression: a survey and empirical analysis
por: Avelino, Juscimara G., et al.
Publicado: (2025)

Practical estimation of the optimal classification error with soft labels and calibration
por: Ushio, Ryota, et al.
Publicado: (2025)

Neighbor displacement-based enhanced synthetic oversampling for multiclass imbalanced data
por: Putrama, I Made, et al.
Publicado: (2025)

Tree species classification at the pixel-level using deep learning and multispectral time series in an imbalanced context
por: Mouret, Florian, et al.
Publicado: (2024)

CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
por: Liu, Zhining, et al.
Publicado: (2025)

Sharp analysis of out-of-distribution error for "importance-weighted" estimators in the overparameterized regime
por: Lai, Kuo-Wei, et al.
Publicado: (2024)

$F_β$-plot -- a visual tool for evaluating imbalanced data classifiers
por: Wojciechowski, Szymon, et al.
Publicado: (2024)

Generalization bounds for regression and classification on adaptive covering input domains
por: Hwang, Wen-Liang
Publicado: (2024)

Enumerating the k-fold configurations in multi-class classification problems
por: Fazekas, Attila, et al.
Publicado: (2024)

Unsupervised anomaly detection algorithms on real-world data: how many do we need?
por: Bouman, Roel, et al.
Publicado: (2023)