Results 61 to 70 of about 46,171 (307)
Posterior Re-calibration for Imbalanced Datasets
CoRR, 2020 Accepted to NeurIPS ...
Junjiao Tian +4 more
openaire +3 more sources
Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets [PDF]
ETRI Journal, 2017 Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned ...
GuiPing Wang, JianXi Yang, Ren Li
openaire +2 more sources
Imbalanced Dataset Optimization with New Resampling Techniques
, 2021 Imbalanced datasets, which are very common in many application fields, represent a formidable problem for most of the machine learning algorithms. On the other hand, such algorithms are being extensively applied in many areas, showing promising results ...
Ivan Letteri +3 more
core +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
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
Tongxin xuebao, 2023 To deal with the abnormal data flow attacks faced by the marine meteorological sensor network (MMSN), analyze the security mechanism, and aim at the complex and huge network structure and the extremely imbalanced data flow in the nodes, the intrusion ...
Xin SU +3 more
doaj +2 more sources
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
Journal of Intelligent SystemsProblem: Data imbalance in medical datasets poses significant challenges for the performance of machine learning models, particularly in classifying Alzheimer’s disease (AD).
Almalki Hassan +2 more
doaj +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
RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification
Journal of King Saud University: Computer and Information Sciences, 2022 Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning.
Ahmed Arafa +3 more
doaj +1 more source