Results 41 to 50 of about 40,989 (265)
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
FEBS Letters, EarlyView.Drosophila park mutants serve as a model for Parkinson's disease. We used this strain to investigate the connection between oxidative stress and the circadian clock mechanism. We showed that increased oxidative stress affects the physiology of pacemaker cells, disrupting their daily structural plasticity. Lack of rhythmic signaling from pacemaker cells
Kamila Zientara +3 more
wiley +1 more source
A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems
IEEE AccessMulti-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented.
Jiao Wang, Norhashidah Awang
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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 +2 more
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
IJAIN (International Journal of Advances in Intelligent Informatics), 2019 Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input.
A’inur A’fifah Amri +2 more
doaj +1 more source
Applied Sciences, 2022 The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples.
Zafar Mahmood +6 more
doaj +1 more source
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
Molecular Oncology, EarlyView.Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson +9 more
wiley +1 more source
Regularization for Deep Imbalanced Regression Based on Quantitative Relationship
Big Data Mining and AnalyticsImbalanced datasets are prevalent in real life. The imbalanced datasets pose challenges for classification and regression tasks. Compared to imbalanced classification, imbalanced regression deals with continuous labels. The positional relationship of the
Heng Zhao, Jiehao Chen, Xianghua Fu
doaj +1 more source