Results 21 to 30 of about 539,097 (330)
Multi-fairness Under Class-Imbalance
, 2022 Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the often underrepresented protected group (e.g. female, non-white, etc.) in the critical minority class.
Arjun Roy 0001 +2 more
openaire +2 more sources
Stop Oversampling for Class Imbalance Learning: A Review
IEEE Access, 2022 For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is,
Ahmad S. Tarawneh +3 more
doaj +1 more source
Measuring the class-imbalance extent of multi-class problems [PDF]
Pattern Recognition Letters, 2017 TIN2013-41272P, IT609-13, AP2008 ...
Jonathan Ortigosa-Hernández +2 more
openaire +2 more sources
A Survey on Class Imbalance in Federated Learning
CoRR, 2023 Federated learning, which allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data, is an emerging distributed learning technique due to its nature of privacy preservation. However, it has been found that models trained with federated learning usually have worse performance than ...
Jing Zhang +3 more
openaire +2 more sources
Progressive Boosting for Class Imbalance
CoRR, 2017 Pattern recognition applications often suffer from skewed data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using skewed data tend to recognize the majority class better than the minority class of interest.
Roghayeh Soleymani +2 more
openaire +2 more sources
Addressing class imbalance in soil movement predictions [PDF]
Natural Hazards and Earth System SciencesLandslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat.
P. Kumar +3 more
doaj +1 more source
Class Balanced Loss for Image Classification
IEEE Access, 2020 In the study of image classification, neural network learning relies heavily on datasets. Due to variability in the difficulty of collecting images in reality, datasets tend to have class imbalance problems, which undoubtedly increases the difficulty of ...
Lin Wang +4 more
doaj +1 more source
Effects of spin imbalance on the electric-field driven quantum dissipationless spin current in $p$-doped Semiconductors [PDF]
, 2004 It was proposed recently by Murakami et al. [Science \textbf{301}, 1348(2003)] that in a large class of $p$-doped semiconductors, an applied electric field can drive a quantum dissipationless spin current in the direction perpendicular to the electric ...
G. Sundaram +3 more
core +2 more sources
An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]
, 2012 The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
A Estabrooks +33 more
core +3 more sources
Generative Adversarial Networks for Bitcoin Data Augmentation [PDF]
, 2020 In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches.
Barrio, Xabier Etxeberria +4 more
core +2 more sources