Results 41 to 50 of about 40,795 (265)
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
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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
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
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
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
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
IEEE Access, 2023 Medical data often exhibit class imbalance, which poses a challenge in classification tasks. To solve this problem, data augmentation techniques are used to balance the data.
Sanghoon Choi +4 more
doaj +1 more source
Molecular Oncology, EarlyView.Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary +1 more
wiley +1 more source
Computer Vision in Spiritual Seeing: Recognition of Christian Saints in Orthodox Iconography
InformationChristian Orthodox iconography is a fundamental element of the religious cultural heritage of many countries. Iconoclasm, vandalism, and the passage of time ruined the appearance of icons, making it difficult to recognize the depicted saints.
Ilias I. Sidiropoulos +3 more
doaj +1 more source