Results 101 to 110 of about 539,097 (330)
Active Learning Strategies for the Class Imbalance Problem
IEEE AccessRecent advancements in data technology have greatly expanded data availability; however, extracting valuable information remains challenging due to high annotation costs and severe class imbalance.
Bokyung Amy Kwon, Kyungtae Kang
doaj +1 more source
Addressing class imbalance in trust and stereotype assessment [PDF]
, 2019 Trust, reputation and stereotypes enable agents to identify reliable interaction partners based on past interactions. However, such methods can cause agents to choose the same known partners instead of unknown, but potentially better, alternatives ...
Griffiths, Nathan, Player, C.
core
Evolutionarily divergent DUF4465 domains have a common vitamin B12‐binding function
FEBS Open Bio, EarlyView.We show that DUF4465 family proteins, widespread across bacteria from gut microbiomes, hydrothermal vents, and soil, share a common vitamin B12‐binding function. These augmented β‐jellyroll proteins bind vitamin B12 via extended loops. Our findings establish sequence‐diverse DUF4465 proteins as a widespread class of B12‐binding proteins, highlighting ...
Charlea Clarke +4 more
wiley +1 more source
On the Robustness of Compressed Models with Class Imbalance
ComputersDeep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To this end, many model compression techniques proposed in the literature successfully reduce model sizes ...
Baraa Saeed Ali +2 more
openaire +2 more sources
Impact of class-imbalance on multi-class high-dimensional class prediction
Advances in Methodology and Statistics, 2012 The goal of multi-class supervised classification is to develop a rule that accurately predicts the class membership of new samples when the number of classes is larger than two. In this paper we consider high-dimensional class-imbalanced data: the number of variables greatly exceeds the number of samples and the number of samples in each class is not ...
Blagus, Rok, Lusa, Lara
openaire +4 more sources
FEBS Open Bio, EarlyView.In a murine model of myocardial ischemia and reperfusion (MI/R), the CD36 azapeptide ligand MPE‐298 reduces cardiac injury and transiently lowers left ventricular long‐chain fatty acids (LCFAs) accumulation 3 h after reperfusion, accompanied by a decrease of oxidative stress and inflammation‐associated genes' expression in the heart and adipose tissue.
Jade Gauvin +12 more
wiley +1 more source
Improving Model Performance for Predicting Exfiltration Attacks Through Resampling Strategies
International Islamic University Malaysia Engineering JournalAddressing class imbalance is critical in cybersecurity applications, particularly in scenarios like exfiltration detection, where skewed datasets lead to biased predictions and poor generalization for minority classes.
Arif Rahman Hakim +3 more
doaj +1 more source
Aging and Cancer, EarlyView.This study aimed to evaluate the prognostic value of ELN2017 in predicting survival outcomes and to assess the impact of clinical and molecular factors such as age, FLT3 and NPM1 mutations, and allogeneic hematopoietic stem cell transplantation (allo‐HSCT).
Mobina Shrestha +4 more
wiley +1 more source
MKC-SMOTE: A Novel Synthetic Oversampling Method for Multi-Class Imbalanced Data Classification
IEEE AccessThe learning of multi-class imbalance problems presents greater challenges and has fewer research results compared to binary imbalance problems. Resampling techniques are widely employed to address data imbalance problems.
Jiao Wang, Norhashidah Awang
doaj +1 more source