Results 61 to 70 of about 2,374,745 (297)

The Distance-Based Balancing Ensemble Method for Data With a High Imbalance Ratio

IEEE Access, 2019
Many classification tasks suffer from the class imbalance problem that seriously hinders the precision of classifiers. The existing algorithms frequently incorrectly categorize new instances into the majority class.
Dong Chen, Xiao-Jun Wang, Changjun Zhou, Bin Wang   +3 more
doaj   +1 more source

On Hierarchical Composite Endpoints in Pediatric Cancer Supportive Care: Illustrative Examples From Two Multi‐Center Phase‐III Randomized Clinical Trials

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Pediatric supportive care clinical trials often involve multiple clinically important outcomes, complicating trial interpretation. Hierarchical composite endpoints (HCEs) provide a framework to integrate key outcomes according to clinical importance.
Willem H. Collier, Mark Zobeck, Adam J. Esbenshade, Christopher C. Dvorak, Lillian Sung, David Freyer, Sarah Alexander, Etan Orgel, Nicole J. Ullrich, Zach Prudowsky, Brian Fisher, Caitlin W. Elgarten   +11 more
wiley   +1 more source

A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems

IEEE Access
Multi-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented.
Jiao Wang, Norhashidah Awang
doaj   +1 more source

A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets

Abstract and Applied Analysis, 2013
In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers.
Yong Zhang, Dapeng Wang
doaj   +1 more source

Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

IEEE Access, 2022
Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as well as the thriving underground cyber-crime economy to support it. Over the past several years,
Ayesha Siddiqua Dina, A. B. Siddique, D. Manivannan   +2 more
doaj   +1 more source

Plant Disease Detection in Imbalanced Datasets using Efficient Convolutional Neural Networks with Stepwise Transfer Learning

IEEE Access, 2021
Convolutional neural networks have demonstrated state-of-the-art performance in image classification and various other computer vision tasks. Plant disease detection is an important area of deep learning which has been addressed by many recent methods ...
Mobeen Ahmad, Muhammad Abdullah, Hyeonjoon Moon, Dongil Han   +3 more
semanticscholar   +1 more source

Organ‐specific redox imbalances in spinal muscular atrophy mice are partially rescued by SMN antisense oligonucleotides

FEBS Letters, EarlyView.
We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley   +1 more source

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

A Hybrid Approach Handling Imbalanced Datasets [PDF]

, 2009
Several binary classification problems exhibit imbalance in class distribution, influencing system learning. Indeed, traditional machine learning algorithms are biased towards the majority class, thus producing poor predictive accuracy over the minority one. To overcome this limitation, many approaches have been proposed up to now to build artificially
openaire   +2 more sources

Is Diabetic Retinopathy Grading Biased by Imbalanced Datasets?

, 2022
Diabetic retinopathy (DR) is one of the most severe complications of diabetes and the leading cause of vision loss and even blindness. Retinal screening contributes to early detection and treatment of diabetic retinopathy. This eye disease has five stages, namely normal, mild, moderate, severe and proliferative diabetic retinopathy.
Monteiro, Fernando C., Rufino, José
openaire   +3 more sources