Results 31 to 40 of about 39,020 (265)
Binary classification for imbalanced datasets using a novel metric method
Egyptian Informatics JournalThis work proposes a kernel amplification method with non-stationary characteristics for binary classification of non-noisy imbalanced datasets. Our methodology features two key innovations, including that a derived non-stationary kernel construction ...
Jian Zheng +3 more
doaj +1 more source
Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers [PDF]
International Journal of Computational Intelligence Systems, 2012 Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain.
AnaM. Palacios +2 more
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Active Class Incremental Learning for Imbalanced Datasets [PDF]
, 2020 Accepted in IPCV workshop from ...
Eden Belouadah +3 more
openaire +2 more sources
Superensemble classifier for improving predictions in imbalanced datasets [PDF]
Communications in Statistics: Case Studies, Data Analysis and Applications, 2020 Learning from an imbalanced dataset is a tricky proposition. Because these datasets are biased towards one class, most existing classifiers tend not to perform well on minority class examples. Conventional classifiers usually aim to optimize the overall accuracy without considering the relative distribution of each class.
Tanujit Chakraborty +1 more
openaire +2 more sources
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
Imbalanced Data Classification Method Based on LSSASMOTE
IEEE Access, 2023 Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
doaj +1 more source
Posterior Re-calibration for Imbalanced Datasets
CoRR, 2020 Accepted to NeurIPS ...
Junjiao Tian +4 more
openaire +3 more sources
Applied Sciences, 2021 Over the recent years, Industry 4.0 (I4.0) technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have helped achieve intelligent Fault Detection (FD) in manufacturing ...
Jefkine Kafunah +2 more
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
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 +3 more
doaj +1 more source