LIDTA

Keyphrases

Publications

2023

volume 241, 2023

Fifth International Workshop on Learning with Imbalanced Domains: Theory and Applications, 18 September 2023, LIDTA@ECML-PKDD, Turin, Italy. LIDTA 241 (2023)

2022

Ellen Rushe, Brian Mac Namee
Deep Contextual Novelty Detection with Context Prediction. LIDTA (2022)
Xin Yue Song, Nam Dao, Paula Branco
DistSMOGN: Distributed SMOGN for Imbalanced Regression Problems. LIDTA (2022)
Carlos Ortega Vázquez, Jochen De Weerdt, Seppe vanden Broucke
The Hidden Cost of Fraud: An Instance-Dependent Cost-Sensitive Approach for Positive and Unlabeled Learning. LIDTA (2022)
Solander Patricio Lopes Agostinho, João Mendes-Moreira
Probabilistic Metric to measure the imbalance in multi-class problems. LIDTA (2022)
Adam Wojciechowski, Mateusz Lango
Adversarial oversampling for multi-class imbalanced data classification with convolutional neural networks. LIDTA (2022)
Jairo da Silva Freitas Junior, Paulo Henrique Pisani
Performance and model complexity on imbalanced datasets using resampling and cost-sensitive algorithms. LIDTA (2022)
Aymene Mohammed Bouayed, Samuel Deslauriers-Gauthier, Mauro Zucchelli, Rachid Deriche
CNN and diffusion MRI’s 4th degree rotational invariants for Alzheimer’s disease identification. LIDTA (2022)
Joanna Komorniczak, Pawel Ksieniewicz, Michal Wozniak
Data complexity and classification accuracy correlation in oversampling algorithms. LIDTA (2022)
Baptiste Bauvin, Jacques Corbeil, Dominique Benielli, Sokol Koço, Cécile Capponi
Integrating and reporting full multi-view supervised learning experiments using SuMMIT. LIDTA (2022)
Thomas Bonnier, Benjamin Bosch
Assessing the Robustness of Ordinal Classifiers against Imbalanced and Shifting Distributions. LIDTA (2022)
Yiwen Shi, Taha ValizadehAslani, Jing Wang, Ping Ren, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang
Improving Imbalanced Learning by Pre-finetuning with Data Augmentation. LIDTA (2022)
Nuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak, Shuo Wang
4th Workshop on Learning with Imbalanced Domains: Preface. LIDTA (2022)
Sander De Block, Jessa Bekker
Bagging Propensity Weighting: A Robust method for biased PU Learning. LIDTA (2022)
Agnieszka Lipska, Jerzy Stefanowski
The Influence of Multiple Classes on Learning from Imbalanced Data Streams. LIDTA (2022)
Ioannis Antoniadis, Vincent Vercruyssen, Jesse Davis
Systematic Evaluation of CASH Search Strategies for Unsupervised Anomaly Detection. LIDTA (2022)

volume 183, 2022

Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA 2022, Grenoble, France, September 23, 2022 LIDTA 183 (2022)