DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation. [PDF]
Bo W +6 more
europepmc +1 more source
StackingNet: Collective Inference Across Independent AI Foundation Models
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li +4 more
wiley +1 more source
Forest-EMCBE: an evolutionary ensemble learning algorithm for multiclass diagnosis of bacterial pneumonia using the CBC dataset. [PDF]
Shen Y, Xu X, Hao X, Sun C, Lan W.
europepmc +1 more source
Machine learning for land use change analysis in environmental protection areas. [PDF]
Lizcano Toledo MV +7 more
europepmc +1 more source
KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes. [PDF]
Qiu S +6 more
europepmc +1 more source
Enhancing the classification of spectrally similar land use/land cover classes using transfer learning in arid regions. [PDF]
Farag NH +4 more
europepmc +1 more source
Usmile likelihood evaluation provides robust threshold free assessment of binary classification models for balanced and imbalanced datasets. [PDF]
Więckowska B, Guzik P.
europepmc +1 more source
Leveraging 3D Heart Visualisation and Data Balancing Techniques for ECG Classification. [PDF]
Amara K +4 more
europepmc +1 more source
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Assessing the data complexity of imbalanced datasets
Information Sciences, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
André DE CARVALHO +2 more
exaly +2 more sources
Imbalanced Learning in Massive Phishing Datasets
2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020Phishing is one of the major threats facing internet users in today’s work. Such attacks continue costing billions of dollars to companies around the words thus requiring more efficient detection techniques to curb the danger. This paper proposes a big data friendly implementation of Multiclass Imbalance Learning in Ensembles through Selective Sampling
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