Results 51 to 60 of about 539,097 (330)

Lattice and Imbalance Informed Multi-Label Learning

IEEE Access, 2020
In a multi-label dataset, an instance is given a single representation across all possible labels. Despite the mutual sharing of instances among the labels, the membership of the instances vary from label to label.
Payel Sadhukhan, Sarbani Palit
doaj   +1 more source

Misclassification analysis for the class imbalance problem [PDF]

, 2010
In classification, the class imbalance issue normally causes the learning algorithm to be dominated by the majority classes and the features of the minority classes are sometimes ignored.
Fung, C.C., Jeatrakul, P., Takama, Y., Wong, K.W.   +3 more
core   +1 more source

Effects of the Fluid Replacement Method During Online Hemodiafiltration on the Solute Removal Performance and Biocompatibility Using the Asymmetric Cellulose Triacetate Membrane

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Introduction Pre‐dilution online hemodiafiltration (Pre‐HDF) is predominantly used in Japan, whereas post‐dilution online HDF (Post‐HDF) is more common in Europe. An asymmetric cellulose triacetate (ATA) membrane may improve biocompatibility.
Kenji Sakurai, Yoshitaka Kurihara, Fumi Yamauchi, Hiromi Hosoya, Takeshi Saito   +4 more
wiley   +1 more source

Revealing the structure of land plant photosystem II: the journey from negative‐stain EM to cryo‐EM

FEBS Letters, EarlyView.
Advances in cryo‐EM have revealed the detailed structure of Photosystem II, a key protein complex driving photosynthesis. This review traces the journey from early low‐resolution images to high‐resolution models, highlighting how these discoveries deepen our understanding of light harvesting and energy conversion in plants.
Roman Kouřil
wiley   +1 more source

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

, 2019
Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance.
Bozorgtabar, Behzad, Ebner, Lukas, Mahapatra, Dwarikanath, Pollinger, Alexander, Reyes, Mauricio, Thiran, Jean-Phillipe, von Teng, Hendrik   +6 more
core   +1 more source

Spatiotemporal and quantitative analyses of phosphoinositides – fluorescent probe—and mass spectrometry‐based approaches

FEBS Letters, EarlyView.
Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho, Shin Morioka, Junko Sasaki, Takehiko Sasaki   +3 more
wiley   +1 more source

Analysis of classification metric behaviour under class imbalance

Egyptian Informatics Journal
Class imbalance is the phenomenon defined as skewed target variable distributions in a dataset. In other words class imbalance occurs when a dataset has an unequal proportion of target variables assigned to the instances in the dataset.
Jean-Pierre van Zyl, Andries Petrus Engelbrecht   +1 more
doaj   +1 more source

In situ molecular organization and heterogeneity of the Legionella Dot/Icm T4SS

FEBS Letters, EarlyView.
We present a nearly complete in situ model of the Legionella Dot/Icm type IV secretion system, revealing its central secretion channel and identifying new components. Using cryo‐electron tomography with AI‐based modeling, our work highlights the structure, variability, and mechanism of this complex nanomachine, advancing understanding of bacterial ...
Przemysław Dutka, Yuxi Liu, Stefano Maggi, Debnath Ghosal, Jue Wang, Prashant P. Damke, Stephen D. Carter, Wei Zhao, Sukhithasri Vijayrajratnam, Joseph P. Vogel, Carrie L. Shaffer, Grant J. Jensen   +11 more
wiley   +1 more source

Deep Over-sampling Framework for Classifying Imbalanced Data

, 2017
Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured
B Krawczyk, C Dong, G Hinton, GE Hinton, H He, KQ Weinberger, MD Zeiler, NV Chawla, NV Chawla, P Jeatrakul, RA Dunne, S Ando, S Köknar-Tezel, Y Bengio, Y Lecun, ZH Zhou   +15 more
core   +1 more source

Exploring uplift modeling with high class imbalance

Data Mining and Knowledge Discovery, 2022
Abstract Uplift modeling refers to individual level causal inference. Existing research on the topic ignores one prevalent and important aspect: high class imbalance. For instance in online environments uplift modeling is used to optimally target ads and discounts, but very few users ever end up clicking an ad or buying.
Otto Nyberg, Arto Klami
openaire   +2 more sources