Results 31 to 40 of about 13,846 (260)

FLEX-SMOTE

, 2023
FLEX-SMOTE: Synthetic Over-sampling TEchnique that Flexibly Adjusts to Different Minority Class Distributions
openaire   +1 more source

Cancer‐Associated BCL‐2 Mutants Reveal Mechanisms Towards Venetoclax Resistance

Advanced Science, EarlyView.
Venetoclax (VEN) resistance in chronic lymphocytic leukemia arises from diverse BCL2 mutations. We map mechanisms contributing to VEN resistance across common BCL‐2 variants. G101V and D103Y reduce drug binding and increase sequestration of pro‐apoptotic proteins. V156D blocks VEN allosterically.
Jonas Aufdermauer, Ian de Ridder, Mahjoobeh Ehsani, Marasim Khan, Justin Kale, Lukas P. Frenzel, David Andrews, Spyridoula Karamanou, Ana J. Garcia‐Saez, Geert Bultynck   +9 more
wiley   +1 more source

Comprehensive Analysis of Data Augmentation Methods in Classification for an Imbalanced Epilepsy Dataset

IEEE Access
Imbalanced class distribution reduces the generalizability of classifiers in EEG-based epilepsy detection. This study examines the impact of the synthetic minority oversampling technique (SMOTE) and its variants on imbalanced electroencephalography (EEG)
Ahmet Gokay Calis, Halit Ergezer
doaj   +1 more source

A Panel of Circulating Exosomal sncRNAs Associated With Lung Cancer Risk up to 10 Years in Advance

Advanced Science, EarlyView.
Lung cancer is often diagnosed too late, and current screening overlooks many people at risk. In a long‐term study of smokers, a panel of small non‐coding RNAs carried in blood exosomes signals elevated lung cancer risk up to ten years before diagnosis, pointing toward a blood‐based tool for earlier risk detection.
Zhuokun Feng, Masaki Nasu, Ayman Abdul‐Ghani, Yu Chen, Shaoqiu Chen, Isam Mohd Ibrahim, Hanqiu Zhang, Lauren Higa, Zitong Gao, Hua Yang, Raphael Bueno, Loïc Le Marchand, Youping Deng   +12 more
wiley   +1 more source

Addressing imbalanced data classification with Cluster-Based Reduced Noise SMOTE.

PLoS ONE
In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced Noise
Javad Hemmatian, Rassoul Hajizadeh, Fakhroddin Nazari   +2 more
doaj   +1 more source

FADA-SMOTE-Ms: Fuzzy Adaptative Smote-Based Methods

IEEE Access
The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known methods to solve the unequal class distribution problem in imbalanced datasets. However, it has three shortcomings: (1) it may cause the over-generalization problem due to oversampling of noisy samples, (2) over-sampling of uninformative samples, and (3) increasing the
Roudani Mohammed, Karim El Moutaouakil
openaire   +2 more sources

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets

Scientific Reports
Class imbalance poses a significant challenge in water quality classification, often leading to biased predictions and diminished accuracy for minority classes.
Norashikin Nasaruddin, Nurulkamal Masseran, Wan Mohd Razi Idris, Ahmad Zia Ul-Saufie   +3 more
doaj   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
wiley   +1 more source