Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach
Sarah Vluymans, Alberto Fernández, Yvan Saeys, et al.
Knowledge and Information Systems (2017) Vol. 56, Iss. 1, pp. 55-84
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme
Jingjun Bi, Chongsheng Zhang
Knowledge-Based Systems (2018) Vol. 158, pp. 81-93
Closed Access | Times Cited: 181
OFS-Density: A novel online streaming feature selection method
Peng Zhou, Xuegang Hu, Peipei Li, et al.
Pattern Recognition (2018) Vol. 86, pp. 48-61
Open Access | Times Cited: 84
A threshold self-setting condition monitoring scheme for wind turbine generator bearings based on deep convolutional generative adversarial networks
Peng Chen, Yu Li, Kesheng Wang, et al.
Measurement (2020) Vol. 167, pp. 108234-108234
Closed Access | Times Cited: 76
Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering
Qianmu Li, Yanjun Song, Jing Zhang, et al.
Expert Systems with Applications (2019) Vol. 147, pp. 113152-113152
Closed Access | Times Cited: 54
A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data
Lior Shabtay, Philippe Fournier‐Viger, Rami Yaari, et al.
Information Sciences (2020) Vol. 553, pp. 353-375
Closed Access | Times Cited: 52
Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review
Wanting Ji, Yan Pang, Xiaoyun Jia, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2021) Vol. 11, Iss. 3
Closed Access | Times Cited: 46
AFNFS: Adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data
Lin Sun, Mengmeng Li, Weiping Ding, et al.
Information Sciences (2022) Vol. 612, pp. 724-744
Closed Access | Times Cited: 31
Semi-Supervised Deep Fuzzy C-Mean Clustering for Imbalanced Multi-Class Classification
Ali Arshad, Saman Riaz, Licheng Jiao
IEEE Access (2019) Vol. 7, pp. 28100-28112
Open Access | Times Cited: 46
Interval–valued fuzzy and intuitionistic fuzzy–KNN for imbalanced data classification
Saeed Zeraatkar, Fatemeh Afsari
Expert Systems with Applications (2021) Vol. 184, pp. 115510-115510
Closed Access | Times Cited: 33
Fuzzy rough nearest neighbour methods for detecting emotions, hate speech and irony
Olha Kaminska, Chris Cornelis, Véronique Hoste
Information Sciences (2023) Vol. 625, pp. 521-535
Open Access | Times Cited: 13
Evolutionary optimization of the area under precision-recall curve for classifying imbalanced multi-class data
Marwa Chabbouh, Marwa Chabbouh, Efrén Mezura‐Montes, et al.
Journal of Heuristics (2025) Vol. 31, Iss. 1
Closed Access
Constructive sample partition-based parameter-free sampling for class-overlapped imbalanced data classification
Weiqing Wang, Yuanting Yan, Peng Zhou, et al.
Applied Intelligence (2025) Vol. 55, Iss. 6
Closed Access
Weight selection strategies for ordered weighted average based fuzzy rough sets
Sarah Vluymans, Neil Mac Parthaláin, Chris Cornelis, et al.
Information Sciences (2019) Vol. 501, pp. 155-171
Open Access | Times Cited: 39
Random Balance ensembles for multiclass imbalance learning
Juan J. Rodríguez, José-Francisco Díez-Pastor, Álvar Arnaiz‐González, et al.
Knowledge-Based Systems (2019) Vol. 193, pp. 105434-105434
Open Access | Times Cited: 33
A combined entropy-based approach for a proactive credit scoring
Salvatore Carta, Anselmo Ferreira, Diego Reforgiato Recupero, et al.
Engineering Applications of Artificial Intelligence (2019) Vol. 87, pp. 103292-103292
Closed Access | Times Cited: 32
Emotional classification of music using neural networks with the MediaEval dataset
Yesid Ospitia Medina, José Ramón Beltrán, Sandra Baldassarri
Personal and Ubiquitous Computing (2020) Vol. 26, Iss. 4, pp. 1237-1249
Closed Access | Times Cited: 25
fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
Lecture notes in computer science (2020), pp. 491-499
Open Access | Times Cited: 21
Applications of rough sets in big data analysis: An overview
Piotr Pięta, Tomasz Szmuc
International Journal of Applied Mathematics and Computer Science (2021) Vol. 31, Iss. 4
Open Access | Times Cited: 18
Scalable Approximate FRNN-OWA Classification
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
IEEE Transactions on Fuzzy Systems (2019) Vol. 28, Iss. 5, pp. 929-938
Open Access | Times Cited: 16
Fuzzy Rough Nearest Neighbour Methods for Aspect-Based Sentiment Analysis
Olha Kaminska, Chris Cornelis, Véronique Hoste
Electronics (2023) Vol. 12, Iss. 5, pp. 1088-1088
Open Access | Times Cited: 5
Perceptual Borderline for Balancing Multi-Class Spontaneous Emotional Data
Leila Ben Letaifa, M. Inés Torres
IEEE Access (2021) Vol. 9, pp. 55939-55954
Open Access | Times Cited: 11
An effective method using clustering-based adaptive decomposition and editing-based diversified oversamping for multi-class imbalanced datasets
Xiangtao Chen, Lan Zhang, Xiaohui Wei, et al.
Applied Intelligence (2020) Vol. 51, Iss. 4, pp. 1918-1933
Closed Access | Times Cited: 10
A novel fuzzy rule extraction approach using Gaussian kernel-based granular computing
Guangyao Dai, Yichen Hu, Yu Yang, et al.
Knowledge and Information Systems (2019) Vol. 61, Iss. 2, pp. 821-846
Closed Access | Times Cited: 10
Semi-supervised Learning Algorithm Based on Linear Lie Group for Imbalanced Multi-class Classification
Chengjun Xu, Guobin Zhu
Neural Processing Letters (2020) Vol. 52, Iss. 1, pp. 869-889
Closed Access | Times Cited: 9
Imbalanced Classification with Multiple Classes
Alberto Fernández, Salvador García, Mikel Galar, et al.
Springer eBooks (2018), pp. 197-226
Closed Access | Times Cited: 8
Page 1 - Next Page

Scroll to top