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Deep learning regularization in imbalanced data
2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), 2020Deep neural networks are known to have a large number of parameters which can lead to overfitting. As a result various regularization methods designed to mitigate the model overfitting have become an indispensable part of many neural network architectures. However, it remains unclear which regularization methods are the most effective.
Firuz Kamalov, Ho Hon Leung
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Imbalanced evolving self-organizing learning
Neurocomputing, 2014In this paper, a hybrid learning model of imbalanced evolving self-organizing maps (IESOMs) is proposed to address the imbalanced learning problems. In our approach, we propose to modify the classic SOM learning rule to search the winner neuron based on energy function by minimally reducing local error in the competitive learning phase.
Cai, Qiao, He, Haibo, Man, Hong
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2011
An imbalanced training dataset can pose serious problems for many real-world data-mining tasks that conduct supervised learning. In this chapter,\(^\dagger\) we present a kernel-boundary-alignment algorithm, which considers training-data imbalance as prior information to augment SVMs to improve class-prediction accuracy.
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An imbalanced training dataset can pose serious problems for many real-world data-mining tasks that conduct supervised learning. In this chapter,\(^\dagger\) we present a kernel-boundary-alignment algorithm, which considers training-data imbalance as prior information to augment SVMs to improve class-prediction accuracy.
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Learning Deep Representation for Imbalanced Classification
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016Data in vision domain often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re ...
Chen Huang 0001 +3 more
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Self-paced Learning for Imbalanced Data
2016In this paper, we propose a novel training paradigm that combines two learning strategies: cost-sensitive and self-paced learning. This learning approach can be applied to the decision problems where highly imbalanced data is used during training process.
Maciej Zieba +2 more
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Imbalanced Clustering With Theoretical Learning Bounds
IEEE Transactions on Knowledge and Data Engineering, 2023Jing Zhang 0064, Hong Tao, Chenping Hou
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Imbalanced SVM Learning with Margin Compensation
2008The paper surveys the previous solutions and proposes further a new solution based on the cost-sensitive learning for solving the imbalanced dataset learning problem in the support vector machines. The general idea of cost-sensitive approach is to adopt an inverse proportional penalization scheme for dealing with the problem and forms a penalty ...
Chan-Yun Yang +3 more
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Decoupled Imbalanced Label Distribution Learning
Proceedings of the Thirty-Fourth International Joint Conference on Artificial IntelligenceLabel Distribution Learning (LDL) has been successfully implemented in numerous practical applications. However, the imbalance in label distributions presents a significant challenge due to the substantial variation in annotation information. To tackle this issue, we introduce Decoupled Imbalance Label Distribution Learning (DILDL), which decomposes ...
Yongbiao Gao +5 more
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Multiset Feature Learning for Highly Imbalanced Data Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Xiao-Yuan Jing +2 more
exaly
Federated Fuzzy Learning with Imbalanced Data
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021Lukas Johannes Dust +5 more
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