Results 71 to 80 of about 141,868 (281)

Learning Ensembles in the Presence of Imbalanced Classes

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019
Class imbalance occurs when data classes are not equally represented. Generally, it occurs when some classes represent rare events, while the other classes represent the counterpart of these events. Rare events, especially those that may have a negative impact, often require informed decision-making in a timely manner. However, class imbalance is known
Amal Saadallah, Nico Piatkowski, Felix Finkeldey, Petra Wiederkehr, Katharina Morik   +4 more
openaire   +1 more source

A return to normality: A descriptive qualitative interview study exploring the patient experience of gout flare resolution

Arthritis Care &Research, Accepted Article.
Objectives While the definition of a gout flare is well established, the state of gout flare resolution has not yet been defined. This study aimed to explore patients’ experiences and perceptions of gout flare resolution. Methods Semi‐structured interviews were conducted with 24 people with gout, guided by open‐ended questions exploring their ...
Sarah Stewart, Anne Horne, Angelo Gaffo, Lisa K Stamp, N Lawrence Edwards, Nicola Dalbeth   +5 more
wiley   +1 more source

Feature selection for classification using WGCNA and Spread Sub-Sample for an imbalanced rheumatoid arthritis RNASEQ data

Informatics in Medicine Unlocked, 2023
An imbalanced classification problem occurs when the distribution of samples among different classes is uneven or biased. Handling small and imbalanced training datasets poses a notable challenge in machine learning, especially in domains such as ...
Consolata Gakii, Victoria Mukami, Boaz Too   +2 more
doaj   +1 more source

A comprehensive survey on imbalanced data learning

Frontiers of Computer Science
Abstract With the expansion of data availability, machine learning (ML) has achieved remarkable breakthroughs in both academia and industry. However, imbalanced data distributions are prevalent in various types of raw data and severely hinder the performance of ML by biasing the decision-making processes.
Gao, Xinyi, Xie, Dongting, Zhang, Yihang, Wang, Zhengren, Chen, Chong, He, Conghui, Yin, Hongzhi, Zhang, Wentao   +7 more
openaire   +4 more sources

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

An Integrated Framework of Positive-Unlabeled and Imbalanced Learning for Landslide Susceptibility Mapping

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Machine learning is pivotal in data-driven landslide susceptibility mapping (LSM). However, the uncertainty of negative samples and the imbalance between positive and negative samples, which leads to misjudgments and overestimation, remain ongoing ...
Zijin Fu, Hao Ma, Fawu Wang, Jie Dou, Bo Zhang, Zhice Fang   +5 more
doaj   +1 more source

A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection

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

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Down Syndrome Prediction Using a Cascaded Machine Learning Framework Designed for Imbalanced and Feature-correlated Data

IEEE Access, 2019
Down syndrome (DS) caused by the presence of part or all of a third copy of chromosome 21 is the most common form of aneuploidy. The prenatal screening for DS is a key component of antenatal care and is recommended to be universally offered to women ...
Ling Li, Wanying Liu, Hongguo Zhang, Yuting Jiang, Xiaonan Hu, Ruizhi Liu   +5 more
doaj   +1 more source

From Wafers to Electrodes: Transferring Automatic Optical Inspection (AOI) for Multiscale Characterization of Smart Battery Manufacturing

Advanced Functional Materials, EarlyView.
Automat optical inspection (AOI) techniques in semiconductor fabrication can be leveraged in battery manufacturing, enabling scalable detection and analysis of electrode‐ and cell‐level imperfections through AI‐driven analytics and a digital‐twin framework.
Jianyu Li, Ertao Hu, Wei Wei, Feifei Shi
wiley   +1 more source