Results 11 to 20 of about 539,097 (330)

Survey on deep learning with class imbalance [PDF]

Journal of Big Data, 2019
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real ...
Justin M. Johnson, Taghi M. Khoshgoftaar
doaj   +2 more sources

Effects of Class Imbalance Countermeasures on Interpretability

IEEE Access
The widespread use of artificial intelligence (AI) in more and more real-world applications is accompanied by challenges that are not obvious at first glance. In machine learning, class imbalance, characterized by an imbalance in the frequency of classes,
David Cemernek, Shafaq Siddiqi, Roman Kern   +2 more
doaj   +2 more sources

Federated Learning with Class Imbalance Reduction [PDF]

2021 29th European Signal Processing Conference (EUSIPCO), 2021
Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged ...
Miao Yang, Akitanoshou Wong, Hongbin Zhu, Haifeng Wang, Hua Qian   +4 more
openaire   +2 more sources

Few-Shot Learning With Class Imbalance

IEEE Transactions on Artificial Intelligence, 2023
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood
Mateusz Ochal, Massimiliano Patacchiola, Jose Vazquez, Amos J. Storkey, Sen Wang 0002   +4 more
openaire   +3 more sources

Class Uncertainty: A Measure to Mitigate Class Imbalance

CoRR, 2023
Class-wise characteristics of training examples affect the performance of deep classifiers. A well-studied example is when the number of training examples of classes follows a long-tailed distribution, a situation that is likely to yield sub-optimal performance for under-represented classes.
Zeynep Sonat Baltaci, Kemal Oksuz, Selim Kuzucu, Kivanc Tezoren, Berkin Kerim Konar, Alpay Ozkan, Emre Akbas, Sinan Kalkan   +7 more
openaire   +2 more sources

ADASYN-LOF Algorithm for Imbalanced Tornado Samples

Atmosphere, 2022
Early warning and forecasting of tornadoes began to combine artificial intelligence (AI) and machine learning (ML) algorithms to improve identification efficiency in the past few years.
Zhipeng Qing, Qiangyu Zeng, Hao Wang, Yin Liu, Taisong Xiong, Shihao Zhang   +5 more
doaj   +1 more source

Class Imbalance Learning [PDF]

Advanced Computing and Communications, 2017
Data classification task assigns labels to data points using a model that is learned from a collection of pre-labeled data points. The Class Imbalance Learning (CIL) problem is concerned with the performance of classification algorithms in the presence of under-represented data and severe class distribution skews.
Sudarsun Santhiappan, Balaraman Ravindran   +1 more
openaire   +1 more source

Addressing Class Imbalance in Federated Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution and quantity of the training data on the clients' side may lead to significant challenges such as class imbalance and non-IID (non-independent and identically distributed) data,
Lixu Wang, Shichao Xu, Xiao Wang 0012, Qi Zhu 0002   +3 more
openaire   +2 more sources

Work–life balance/imbalance: the dominance of the middle class and the neglect of the working class [PDF]

, 2015
The paper was stimulated by the question of class in work-life debates. The common conclusion from work-life studies is that work-life imbalance is largely a middle class problem.
Anttila, Atkinson, Becker, Belfield, Bell, Bonke, Bonney, Burchell, Chatzitheochari, Collier, Collins, Crompton, Crompton, Cummins, Davis, Dex, Ehrenreich, Eikhof, Eurofound, Eurofound, Fagan, Fagan, FIC (Financial Inclusion Centre), Fleetwood, Ford, Gallie, Gregg, Gregg, Gregory, Hammermesh, Harrison, Hochschild, Jacobs, Lautsch, Levell, Lewis, Lewis, Lippe, Lyonette, MacInnes, McGinnity, Mellor, Merz, Moore, Padley, Pedersen, Phillips, Roberts, Rode, Rubery, Scherer, Schor, Skeggs, Skeggs, Smithson, Ward, Warren, Warren, Warren, Warren, White, Whittaker   +61 more
core   +4 more sources

Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data

Journal of Big Data, 2019
The integrity of modern network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are consistently shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation ...
Chad L. Calvert, Taghi M. Khoshgoftaar
doaj   +1 more source