An adaptive fault diagnosis framework under class-imbalanced conditions based on contrastive augmented deep reinforcement learning
In practical scenarios, it is difficult to acquire fault data from rotating machinery, resulting in
class-imbalanced problems in the fault diagnosis field. Training a fault diagnosis model …
class-imbalanced problems in the fault diagnosis field. Training a fault diagnosis model …
A survey on imbalanced learning: latest research, applications and future directions
Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …
and machine learning. Despite continuous research advancement over the past decades …
An adaptive and late multifusion framework in contextual representation based on evidential deep learning and Dempster–Shafer theory
There is a growing interest in multidisciplinary research in multimodal synthesis technology
to stimulate diversity of modal interpretation in different application contexts. The real …
to stimulate diversity of modal interpretation in different application contexts. The real …
A deep reinforcement learning-based intelligent fault diagnosis framework for rolling bearings under imbalanced datasets
Y Li, Y Wang, X Zhao, Z Chen - Control Engineering Practice, 2024 - Elsevier
Deep learning is a commonly employed technique for fault diagnosis; however, its
effectiveness is contingent upon the presence of balanced data. In real-world industrial …
effectiveness is contingent upon the presence of balanced data. In real-world industrial …
CSLSEP: an ensemble pruning algorithm based on clustering soft label and sorting for facial expression recognition
S Huang, D Li, Z Zhang, Y Wu, Y Tang, X Chen… - Multimedia Systems, 2023 - Springer
Applying ensemble learning to facial expression recognition is an important research field
nowadays, but all may not be better than many, the redundant learners in the classifier pool …
nowadays, but all may not be better than many, the redundant learners in the classifier pool …
PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification
L Djafri - Information Sciences, 2025 - Elsevier
In the field of data mining and machine learning, dealing with imbalanced datasets is one of
the most complex problems. The class imbalance issue significantly affects the classification …
the most complex problems. The class imbalance issue significantly affects the classification …
[PDF][PDF] Performance analysis of samplers and calibrators with various classifiers for asymmetric hydrological data
Asymmetric data classification presents a significant challenge in machine learning (ML).
While ML algorithms are known for their ability to classify symmetric data effectively …
While ML algorithms are known for their ability to classify symmetric data effectively …
Fair, Robust, and Calibrated Deep Learning with Heavy-Tailed Subgroups
LM Hampton - 2023 - dspace.mit.edu
To deploy safe machine learning systems in the real world, we must ensure they are fair,
robust, and calibrated. However, heavy-tails pose a challenge to this mandate, especially …
robust, and calibrated. However, heavy-tails pose a challenge to this mandate, especially …
[HTML][HTML] 基于多标签随机游走的选择性集成方法用于表情识别
黄仕松， **丹杨， 陈星， 唐玉梅， 吴义青 - Software Engineering and …, 2022 - hanspub.org
为了提升分类器集成的性能, 本文提出了一种基于多标签随机游走的选择性集成方法,
该方法将分类器选择问题建模为多标签分类问题, 以灵活有效选择分类器. 首先在训练集样本与 …
该方法将分类器选择问题建模为多标签分类问题, 以灵活有效选择分类器. 首先在训练集样本与 …