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Viewing Publication 1 - 26 from 26

2022

Systematic Evaluation of CASH Search Strategies for Unsupervised Anomaly DetectionIoannis Antoniadis 0001, Vincent Vercruyssen, Jesse Davis. lidta 2022: 8-22 [doi]

Probabilistic Metric to measure the imbalance in multi-class problemsSolander Patricio Lopes Agostinho, João Mendes-Moreira 0001. lidta 2022: 151-162 [doi]

Integrating and reporting full multi-view supervised learning experiments using SuMMITBaptiste Bauvin, Jacques Corbeil, Dominique Benielli, Sokol Koço, Cécile Capponi. lidta 2022: 139-150 [doi]

Bagging Propensity Weighting: A Robust method for biased PU LearningSander De Block, Jessa Bekker. lidta 2022: 23-37 [doi]

Assessing the Robustness of Ordinal Classifiers against Imbalanced and Shifting DistributionsThomas Bonnier, Benjamin Bosch. lidta 2022: 112-126 [doi]

CNN and diffusion MRI’s 4th degree rotational invariants for Alzheimer’s disease identificationAymene Mohammed Bouayed, Samuel Deslauriers-Gauthier, Mauro Zucchelli, Rachid Deriche. lidta 2022: 163-174 [doi]

Performance and model complexity on imbalanced datasets using resampling and cost-sensitive algorithmsJairo da Silva Freitas Junior, Paulo Henrique Pisani. lidta 2022: 83-97 [doi]

Data complexity and classification accuracy correlation in oversampling algorithmsJoanna Komorniczak, Pawel Ksieniewicz, Michal Wozniak 0001. lidta 2022: 175-186 [doi]

The Influence of Multiple Classes on Learning from Imbalanced Data StreamsAgnieszka Lipska, Jerzy Stefanowski. lidta 2022: 187-198 [doi]

Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA 2022, Grenoble, France, September 23, 2022Nuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak 0001, Shuo Wang, editors, Volume 183 of Proceedings of Machine Learning Research, PMLR, 2022. [doi]

4th Workshop on Learning with Imbalanced Domains: PrefaceNuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak 0001, Shuo Wang. lidta 2022: 1-7 [doi]

Deep Contextual Novelty Detection with Context PredictionEllen Rushe, Brian Mac Namee. lidta 2022: 127-138 [doi]

Improving Imbalanced Learning by Pre-finetuning with Data AugmentationYiwen Shi, Taha ValizadehAslani, Jing Wang, Ping Ren, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang. lidta 2022: 68-82 [doi]

DistSMOGN: Distributed SMOGN for Imbalanced Regression ProblemsXin Yue Song, Nam Dao, Paula Branco. lidta 2022: 38-52 [doi]

The Hidden Cost of Fraud: An Instance-Dependent Cost-Sensitive Approach for Positive and Unlabeled LearningCarlos Ortega Vázquez, Jochen De Weerdt, Seppe Vanden Broucke. lidta 2022: 53-67 [doi]

Adversarial oversampling for multi-class imbalanced data classification with convolutional neural networksAdam Wojciechowski, Mateusz Lango. lidta 2022: 98-111 [doi]

2021

GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing CaseArnaud Bougaham, Adrien Bibal, Isabelle Linden, Benoît Frénay. lidta 2021: 104-117 [doi]

BayesBoost: Identifying and Handling Bias Using Synthetic Data GeneratorsBarbara Draghi, Zhenchen Wang, Puja Myles, Allan Tucker. lidta 2021: 49-62 [doi]

Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank StatisticsMyrto Limnios, Nathan Noiry, Stéphan Clémençon. lidta 2021: 63-75 [doi]

Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA 2021, Bilbao, Spain, September 17, 2021Nuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak 0001, Shuo Wang, editors, Volume 154 of Proceedings of Machine Learning Research, PMLR, 2021. [doi]

3rd Workshop on Learning with Imbalanced Domains: PrefaceNuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak 0001, Shuo Wang. lidta 2021: 1-6 [doi]

Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced DataMaria Naklicka, Jerzy Stefanowski. lidta 2021: 90-103 [doi]

Centralised vs decentralised anomaly detection: when local and imbalanced data are beneficialMirko Nardi, Lorenzo Valerio, Andrea Passarella. lidta 2021: 7-20 [doi]

On Oversampling via Generative Adversarial Networks under Different Data Difficulty FactorsEhsan Nazari, Paula Branco. lidta 2021: 76-89 [doi]

ML-NCA: Multi-label Neighbourhood Component AnalysisArjun Pakrashi, Payel Sadhukhan, Brian Mac Namee. lidta 2021: 35-48 [doi]

Online-MC-Queue: Learning from Imbalanced Multi-Class StreamsFarnaz Sadeghi, Herna L. Viktor. lidta 2021: 21-34 [doi]

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