Learning evolving prototypes for imbalanced data stream classification with limited labels DOI
Zhonglin Wu, Hongliang Wang,

Jingxia Guo

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 679, P. 120979 - 120979

Published: June 12, 2024

Language: Английский

Cost-sensitive continuous ensemble kernel learning for imbalanced data streams with concept drift DOI
Ying‐Ying Chen, Xiaowei Yang,

Hong‐Liang Dai

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 284, P. 111272 - 111272

Published: Dec. 13, 2023

Language: Английский

Citations

13
A comprehensive analysis of concept drift locality in data streams DOI
Gabriel Jonas Aguiar, Alberto Cano

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 289, P. 111535 - 111535

Published: Feb. 17, 2024

Language: Английский

Citations

4
BASWE: Balanced Accuracy-Based Sliding Window Ensemble for Classification in Imbalanced Data Streams with Concept Drift DOI
Douglas Amorim de Oliveira, Karina Valdivia Delgado, Marcelo de Souza Lauretto

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 231 - 246

Published: Jan. 1, 2025

Language: Английский

Citations

0
Online deep learning’s role in conquering the challenges of streaming data: a survey DOI Creative Commons
Muhammad Sulaiman, Mina Farmanbar,

Shingo Kagami

et al.

Knowledge and Information Systems, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Abstract In an era defined by the relentless influx of data from diverse sources, ability to harness and extract valuable insights streaming has become paramount. The rapidly evolving realm online learning techniques is tailored specifically for unique challenges posed data. As digital world continues generate vast torrents real-time data, understanding effectively utilizing approaches are pivotal staying ahead in various domains. One primary goals continuously update model with most recent trends while maintaining improving accuracy previous trends. Based on types feedback, tasks can be divided into three categories: full limited without feedback. This survey aims identify analyze key associated including concept drift, catastrophic forgetting, skewed learning, network adaptation, other existing reviews mainly focus a single challenge or two considering scenarios. article also discusses application ethical implications learning. results this provide researchers instructional designers seeking create effective experiences that incorporate feedback addressing challenges. end, some conclusions, remarks, future directions research community provided based findings review.

Language: Английский

Citations

0
RIONIDA: A Novel Algorithm for Imbalanced Data Combining Instance-Based Learning and Rule Induction DOI
Grzegorz Góra, Andrzej Skowron

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 122015 - 122015

Published: Feb. 1, 2025

Language: Английский

Citations

0
Lightweight Deep Learning for sEMG-Based Fingers Position Classification and Embedded System Deployment DOI Creative Commons
Victor H. Benítez, Jesús Pacheco, Guillermo Cuamea Cruz

et al.

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 38181 - 38194

Published: Jan. 1, 2025

Language: Английский

Citations

0
An innovative study of category incremental learning algorithms for arrhythmia detection DOI
Jianchao Feng, Yujuan Si, Yu Zhang

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113346 - 113346

Published: March 1, 2025

Language: Английский

Citations

0
SiameseDuo++: Active learning from data streams with dual augmented siamese networks DOI
Kleanthis Malialis, Stylianos Filippou, Christos G. Panayiotou

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130083 - 130083

Published: March 1, 2025

Language: Английский

Citations

0
Leveraging active learning for ocean data quality assessment: reducing labeling workload and addressing severe data imbalance challenges DOI Creative Commons
Na Li, Yiyang Qi, Ruyue Xin

et al.

International Journal of Data Science and Analytics, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Abstract Oceanic research initiatives like Argo, GLOSS, and EMSO aim to enhance our understanding of the oceans climate through extensive data collection. Maintaining quality collected is essential for effective analysis real-world applications. While automated semi-automated tests can provide real-time or near-real-time validation, thorough control still depends on operator review. Consequently, current Quality Control (QC) processes continue be labor-intensive. Machine Learning (ML) methods, which analyze vast amounts learn complex patterns autonomously, offer significant potential improving QC processes. However, challenges severe disproportion persist ML approaches. This article proposes exploiting active learning (AL) assist experts, reducing their workload by proactively selecting informative points labeling. Targeting distribution challenge, AL, coupled with imbalance-resilient classifiers, enhances model performance in recognizing erroneous points. To mitigate cold-start problem we propose outlier detection initializing significantly annotation costs. Our approach tested generated 5 Argo floats, demonstrating its feasibility lessen labeling experts tackle imbalance. Although experiments are limited scale, findings indicate a promising outlook using ocean assessment, facilitating an framework.

Language: Английский

Citations

0
Improving imbalanced medical image classification through GAN-based data augmentation methods DOI
Hongwei Ding, Nana Huang, Yaoxin Wu

et al.

Pattern Recognition, Journal Year: 2025, Volume and Issue: unknown, P. 111680 - 111680

Published: April 1, 2025

Language: Английский

Citations

0