Results 41 to 50 of about 222,810 (285)
Class Rectification Hard Mining for Imbalanced Deep Learning
, 2017 Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes.
Dong, Qi, Gong, Shaogang, Zhu, Xiatian
core +1 more source
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets [PDF]
, 2016 Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections.
Bettinger, Franck +6 more
core +2 more sources
Data-Centric Optimization Approach for Small, Imbalanced Datasets
Journal of Information and Organizational Sciences, 2023 Data-centric is a newly explored concept, where the attention is given to data optimization methodologies and techniques to improve model performance, rather than focusing on machine learning models and hyperparameter tunning.
Vladislav Tanov
doaj +1 more source
Germline TP53 Mutations Causing Diamond–Blackfan Anemia: A French Report
Pediatric Blood &Cancer, EarlyView.ABSTRACT Diamond–Blackfan anemia is a rare congenital erythroblastopenia typically caused by mutations in ribosomal protein genes. Recently, gain‐of‐function mutations in TP53 have been identified as a novel cause of Diamond–Blackfan anemia. We report two French patients who both harbored a heterozygous TP53 deletion (NM_000546.5: c.1077delA; p ...
Rafael Moisan +6 more
wiley +1 more source
Connection Science, 2019 Learning a classifier from imbalanced data is a challenging problem in Machine learning. A dataset is said to be imbalanced when the number of instances belonging to one class is much less than the number of instances belonging to the other class ...
N. K. Sreeja
doaj +1 more source
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
Processing imbalanced medical data at the data level with assisted-reproduction data as an example
BioData MiningObjective Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification
Junliang Zhu +6 more
doaj +1 more source
Research and application of XGBoost in imbalanced data
International Journal of Distributed Sensor Networks, 2022 As a new and efficient ensemble learning algorithm, XGBoost has been widely applied for its multitudinous advantages, but its classification effect in the case of data imbalance is often not ideal.
Ping Zhang, Yiqiao Jia, Youlin Shang
doaj +1 more source
, 2017 Lung nodule classification is a class imbalanced problem, as nodules are found with much lower frequency than non-nodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority ...
Nakano, Hiroki +3 more
core +1 more source
Ensemble Approach for the Classification of Imbalanced Data [PDF]
, 2009 Ensembles are often capable of greater prediction accuracy than any of their individual members. As a consequence of the diversity between individual base-learners, an ensemble will not suffer from overfitting. On the other hand, in many cases we are dealing with imbalanced data and a classifier which was built using all data has tendency to ignore ...
Vladimir Nikulin +2 more
openaire +3 more sources