Results 51 to 60 of about 4,590,312 (339)

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Journal of Big Data, 2022
The existence of class imbalance in a dataset can greatly bias the classifier towards majority classification. This discrepancy can pose a serious problem for deep learning models, which require copious and diverse amounts of data to learn patterns and ...
Rick Sauber-Cole, T. Khoshgoftaar
semanticscholar   +1 more source

A Class Imbalance Loss for Imbalanced Object Recognition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
The class imbalance problem exists widely in vision data. In these imbalanced datasets, the majority classes dominate the loss and influence the gradient.
Linbin Zhang, Caiguang Zhang, Sinong Quan, Huaxin Xiao, Gangyao Kuang, Li Liu   +5 more
doaj   +1 more source

Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Cancers, 2022
Simple Summary Large-scale medical data carries significant areas of underrepresentation and bias at all levels: clinical, biological, and management.
E. Taşçı, Y. Zhuge, K. Camphausen, A. Krauze   +3 more
semanticscholar   +1 more source

Class Imbalance and Active Learning

, 2013
This chapter focuses on the interaction between active learning (AL) and class imbalance, discussing (i) AL techniques designed specifically for dealing with imbalanced settings, (ii) strategies that leverage AL to overcome the deleterious effects of ...
Ertekin Bolelli, Şeyda, Josh Attenberg, Şeyda Ertekin, Attenberg, Josh   +3 more
core   +2 more sources

Addressing the class imbalance in tabular datasets from a generative adversarial network approach in supervised machine learning

Journal of Algorithms & Computational Technology, 2023
One common issue with datasets used for supervised classification tasks is data imbalance or the unequal distribution of classes within a dataset. The class imbalance may cause biased machine learning models to favor the dominant class, misclassifying ...
Máximo E Sánchez-Gutiérrez, Pedro P González-Pérez   +1 more
doaj   +1 more source

Resampling approaches to handle class imbalance: a review from a data perspective

Journal of Big Data
This article presents a data-driven review of resampling approaches aimed at mitigating the class imbalance problem in machine learning, a widespread issue that limits classifier performance across numerous sectors.
Miguel Carvalho, Armando J. Pinho, Susana Brás   +2 more
semanticscholar   +1 more source

Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data

IEEE Access, 2019
Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the importance of data-based
Solomon H. Ebenuwa, Mhd Saeed Sharif, Mamoun Alazab, Ameer Al-Nemrat   +3 more
doaj   +1 more source

Trainable Undersampling for Class-Imbalance Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most existing undersampling methods is that their data sampling strategies are heuristic-based and independent of the used classifier and evaluation metric. Thus, they may discard informative instances for the classifier during the data sampling.
Minlong Peng, Qi Zhang 0001, Xiaoyu Xing, Tao Gui, Xuanjing Huang 0001, Yu-Gang Jiang 0001, Keyu Ding, Zhigang Chen   +7 more
openaire   +2 more sources

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems [PDF]

, 2011
The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data.
Xia Hong, Hong, Xia, Harris, C. J., Ming Gao, Sheng Chen, Gao, Ming, Chris J. Harris, Chen, Sheng, Gao, M., Harris, Chris, Chen, S.   +10 more
core   +1 more source

Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection

BMC Medical Informatics and Decision Making, 2022
Background Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Despite various studies, class imbalance has always been a difficult issue.
Lijue Liu, Xiaoyue Wu, Shihao Li, Yi Li, Shiyang Tan, Yongping Bai   +5 more
semanticscholar   +1 more source