Results 291 to 300 of about 4,590,312 (339)

BalanceFL: Addressing Class Imbalance in Long-Tail Federated Learning

International Symposium on Information Processing in Sensor Networks, 2022
Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of different nodes without ex-posing the raw data.
Xian Shuai, Yulin Shen, Siyang Jiang, Zhihe Zhao, Zhenyu Yan, Guoliang Xing   +5 more
semanticscholar   +1 more source

Ordinal Class Imbalance with Ranking

2017
Classification datasets, which feature a skewed class distribution, are said to be class imbalance. Traditional methods favor the larger classes. We propose pairwise ranking as a method for imbalance classification so that learning compares pairs of observations from each class, and therefore both contribute equally to the decision boundary.
Ricardo P. M. Cruz, Kelwin Fernandes, Joaquim F. Pinto da Costa, María Pérez-Ortiz 0001, Jaime S. Cardoso 0001   +4 more
openaire   +1 more source

A Closer Look at AUROC and AUPRC under Class Imbalance

Neural Information Processing Systems
In machine learning (ML), a widespread claim is that the area under the precision-recall curve (AUPRC) is a superior metric for model comparison to the area under the receiver operating characteristic (AUROC) for tasks with class imbalance.
Matthew B. A. McDermott, Lasse Hyldig Hansen, Haoran Zhang, G. Angelotti, Jack Gallifant   +4 more
semanticscholar   +1 more source

Class imbalance and the curse of minority hubs

Knowledge-Based Systems, 2013
Most machine learning tasks involve learning from high-dimensional data, which is often quite difficult to handle. Hubness is an aspect of the curse of dimensionality that was shown to be highly detrimental to k-nearest neighbor methods in high-dimensional feature spaces.
Nenad Tomasev, Dunja Mladenic
openaire   +1 more source

Fighting Class Imbalance with Contrastive Learning

2021
Medical image datasets are hard to collect, expensive to label, and often highly imbalanced. The last issue is underestimated, as typical average metrics hardly reveal that the often very important minority classes have a very low accuracy. In this paper, we address this problem by a feature embedding that balances the classes using contrastive ...
Yassine Marrakchi, Osama Makansi, Thomas Brox   +2 more
openaire   +1 more source

Class imbalances versus small disjuncts

ACM SIGKDD Explorations Newsletter, 2004
It is often assumed that class imbalances are responsible for significant losses of performance in standard classifiers. The purpose of this paper is to the question whether class imbalances are truly responsible for this degradation or whether it can be explained in some other way.
Taeho Jo 0001, Nathalie Japkowicz
openaire   +1 more source

Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models

Neural Information Processing Systems
Adam has been shown to outperform gradient descent on large language models by a larger margin than on other tasks, but it is unclear why. We show that a key factor in this performance gap is the heavy-tailed class imbalance found in language tasks. When
Frederik Kunstner, Robin Yadav, Alan Milligan, Mark Schmidt, Alberto Bietti   +4 more
semanticscholar   +1 more source

COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction

Information and Software Technology, 2021
Context: Generally, there are more non-defective instances than defective instances in the datasets used for software defect prediction (SDP), which is referred to as the class imbalance problem.
Shuo Feng, J. Keung, Xiao Yu, Yan Xiao, K. Bennin, M. Kabir, Miao Zhang   +6 more
semanticscholar   +1 more source

A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection

Expert systems with applications, 2021
Class imbalance with overlap is a very challenging problem in electronic fraud transaction detection. Fraudsters have racked their brains to make a fraud transaction as similar as a genuine one in order to avoid being found.
Zhenchuan Li, Mian Huang, Guanjun Liu, Changjun Jiang   +3 more
semanticscholar   +1 more source

The Class Imbalance Problem

2016
We focus on a special category of pattern recognition problems that arise in cases when the set of training patterns is significantly biased towards a particular class of patterns. This is the so-called Class Imbalance Problem which hinders the performance of many standard classifiers.
Dionisios N. Sotiropoulos, George A. Tsihrintzis   +1 more
openaire   +1 more source