Machine Learning and Data Classification

TopicLeading institutions, researchers & key papers

This cluster of papers focuses on the challenges and techniques for learning with noisy labels in machine learning, including methods for hyperparameter optimization, instance selection, robust learning, and automated machine learning. It also explores the use of meta-learning and deep neural networks in handling noisy label problems, particularly in the context of classification tasks and learning from positive and unlabeled data.

Works

IDs:OpenAlex

How has Machine Learning and Data Classification's publication output changed over time?

ScholarIQpublication output · 2014–2025

Output grew100% over the shown period — from 1 works in 2014 to 2 in 2025.

20142016201820192022202320242025

What are the most-cited papers on Machine Learning and Data Classification?

ScholarIQmost cited works

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, Stefano Vassanelli

IEEE Transactions on Neural Networks and Learning Systems. 2018873 CitationsOPEN ACCESS

Flexible Imputation of Missing Data

Hakan Demirtaş

S167961193. 2018726 CitationsOPEN ACCESS

CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

Kuang-Huei Lee, Xiaodong He, Lei Zhang, Linjun Yang

2018488 Citations

Study quality assessment tools

Nicholas Graves, Catherine Wloch, Jennie Wilson, Adrian Barnett, Alex J. Sutton, Nicola J. Cooper, Katharina Merollini, Victoria McCreanor, Qinglu Cheng, Edward Burn, Theresa Lamagni, André Charlett

2016470 Citations

Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning

Wajahat Hussain, Muhammad Faheem Mushtaq, Mobeen Shahroz, Urooj Akram, Ehab Seif Ghith, Mehdi Tlija, Tai-hoon Kim, Imran Ashraf

Scientific Reports. 202534 CitationsOPEN ACCESS