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07/09/2023
Class-Incremental Mixture of Gaussians for Deep Continual Learning
Continual learning models for stationary data focus on learning and reta...
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12/15/2022
Interpretable ML for Imbalanced Data
Deep learning models are being increasingly applied to imbalanced data i...
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07/13/2022
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning
Machine learning (ML) is playing an increasingly important role in rende...
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07/13/2022
Efficient Augmentation for Imbalanced Deep Learning
Deep learning models memorize training data, which hurts their ability t...
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04/07/2022
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Class imbalance poses new challenges when it comes to classifying data s...
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12/21/2021
Mining Drifting Data Streams on a Budget: Combining Active Learning with Self-Labeling
Mining data streams poses a number of challenges, including the continuo...
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07/29/2021
On the combined effect of class imbalance and concept complexity in deep learning
Structural concept complexity, class overlap, and data scarcity are some...
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07/24/2021
Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions
Learning from imbalanced data is among the most challenging areas in con...
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05/05/2021
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
Despite over two decades of progress, imbalanced data is still considere...
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04/24/2021
Class-Incremental Experience Replay for Continual Learning under Concept Drift
Modern machine learning systems need to be able to cope with constantly ...
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04/20/2021
Concept Drift Detection from Multi-Class Imbalanced Data Streams
Continual learning from data streams is among the most important topics ...
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10/14/2020
Adaptive Deep Forest for Online Learning from Drifting Data Streams
Learning from data streams is among the most vital fields of contemporar...
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09/20/2020
Adversarial Concept Drift Detection under Poisoning Attacks for Robust Data Stream Mining
Continuous learning from streaming data is among the most challenging to...
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09/20/2020
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams
Continual learning from streaming data sources becomes more and more pop...
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04/07/2020
Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise
The imbalanced data classification is one of the most crucial tasks faci...
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05/22/2019
Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine
The paper describes how the new technologies and data they generate are ...
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11/17/2018
Monotonic classification: an overview on algorithms, performance measures and data sets
Currently, knowledge discovery in databases is an essential step to iden...
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03/27/2018