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OpenAlex Citation Counts

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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:

Generating diverse and accurate classifier ensembles using multi-objective optimization
Shenkai Gu, Yaochu Jin
(2014), pp. 9-15
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
Ye Ren, Le Zhang, Ponnuthurai Nagaratnam Suganthan
IEEE Computational Intelligence Magazine (2016) Vol. 11, Iss. 1, pp. 41-53
Closed Access | Times Cited: 593
Evolutionary Machine Learning: A Survey
Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 8, pp. 1-35
Open Access | Times Cited: 140
When Does Diversity Help Generalization in Classification Ensembles?
Yijun Bian, Huanhuan Chen
IEEE Transactions on Cybernetics (2021) Vol. 52, Iss. 9, pp. 9059-9075
Open Access | Times Cited: 64
A tree ensemble-based two-stage model for advanced-stage colorectal cancer survival prediction
Yuyan Wang, Dujuan Wang, Xin Ye, et al.
Information Sciences (2018) Vol. 474, pp. 106-124
Closed Access | Times Cited: 62
Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles
M. Paz Sesmero, José Antonio Iglesias, Elena Magán, et al.
Applied Soft Computing (2021) Vol. 111, pp. 107689-107689
Open Access | Times Cited: 36
A novel evolutionary multi-objective ensemble learning approach for forecasting currency exchange rates
Lam Thu Bui, Van Truong Vu, Thi Thu Huong Dinh
Data & Knowledge Engineering (2017) Vol. 114, pp. 40-66
Closed Access | Times Cited: 47
Maximizing diversity by transformed ensemble learning
Shasha Mao, Jiawei Chen, Licheng Jiao, et al.
Applied Soft Computing (2019) Vol. 82, pp. 105580-105580
Closed Access | Times Cited: 39
Multi‐objective ensemble generation
Shenkai Gu, Ran Cheng, Yaochu Jin
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2015) Vol. 5, Iss. 5, pp. 234-245
Closed Access | Times Cited: 38
A hierarchical fusion framework to integrate homogeneous and heterogeneous classifiers for medical decision-making
Linjing Wang, Tianlan Mo, Xuetao Wang, et al.
Knowledge-Based Systems (2020) Vol. 212, pp. 106517-106517
Closed Access | Times Cited: 27
A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers
Zohaib Jan, Brijesh Verma
IEEE Access (2019) Vol. 7, pp. 156360-156373
Open Access | Times Cited: 26
A bi-objective optimization method to produce a near-optimal number of classifiers and increase diversity in Bagging
Shahrokh Asadi, Seyed Ehsan Roshan
Knowledge-Based Systems (2020) Vol. 213, pp. 106656-106656
Closed Access | Times Cited: 26
A survey of evolutionary algorithms for supervised ensemble learning
Henry E. L. Cagnini, Silvia Cristina Nunes das Dores, Alex A. Freitas, et al.
The Knowledge Engineering Review (2023) Vol. 38
Open Access | Times Cited: 8
Automated Ensemble for Deep Learning Inference on Edge Computing Platforms
Yang Bai, Lixing Chen, Mohamed Abdel-Mottaleb, et al.
IEEE Internet of Things Journal (2021) Vol. 9, Iss. 6, pp. 4202-4213
Open Access | Times Cited: 14
Classifier ensemble with evolutionary optimisation enforced random projections
Tianlan Mo, Linjing Wang, Yuliang Wu, et al.
Expert Systems with Applications (2023) Vol. 222, pp. 119845-119845
Closed Access | Times Cited: 4
A multi-objective optimization design framework for ensemble generation
Víctor Henrique Alves Ribeiro, Gilberto Reynoso-Meza
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2018), pp. 1882-1885
Closed Access | Times Cited: 12
A holistic multi-objective optimization design procedure for ensemble member generation and selection
Víctor Henrique Alves Ribeiro, Gilberto Reynoso-Meza
Applied Soft Computing (2019) Vol. 83, pp. 105664-105664
Closed Access | Times Cited: 10
MEG: Multi-objective Ensemble Generation for Software Defect Prediction
Rebecca Moussa, Giovani Guizzo, Federica Sarro
(2022), pp. 159-170
Open Access | Times Cited: 6
Multi-objective Support Vector Machines Ensemble Generation for Water Quality Monitoring
Víctor Henrique Alves Ribeiro, Gilberto Reynoso-Meza
2022 IEEE Congress on Evolutionary Computation (CEC) (2018), pp. 1-6
Closed Access | Times Cited: 8
Ensemble Classifier Optimization by Reducing Input Features and Base Classifiers
Zohaib Jan, Brijesh Verma
2022 IEEE Congress on Evolutionary Computation (CEC) (2019), pp. 1580-1587
Closed Access | Times Cited: 8
Development of ensemble learning classification with density peak decomposition-based evolutionary multi-objective optimization
SeyedEhsan Roshan, Shahrokh Asadi
International Journal of Machine Learning and Cybernetics (2021) Vol. 12, Iss. 6, pp. 1737-1751
Closed Access | Times Cited: 7
Investigating Accuracy and Diversity in Heterogeneous Ensembles for Breast Cancer Classification
Bouchra El Ouassif, Ali Idri, Mohamed Hosni
Lecture notes in computer science (2021), pp. 263-281
Closed Access | Times Cited: 7
A multi-objective ensemble learning approach based on the non-dominated sorting differential evolution for forecasting currency exchange rates
Dinh Thi Thu Huong, Van Truong Vu, Lam Thu Bui
(2016), pp. 96-102
Closed Access | Times Cited: 6
ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM
Mohd Shamrie Sainin, Rayner Alfred, Faudziah Ahmad
Journal of Information and Communication Technology (2021) Vol. 20
Open Access | Times Cited: 6
Multi-objective Search for Comprehensible Rule Ensembles
Jerzy Błaszczyński, Bartosz Prusak, Roman Słowiński
Lecture notes in computer science (2016), pp. 503-513
Closed Access | Times Cited: 4
Deep learning algorithms based text classifier
Arthi Venkataraman
(2016), pp. 220-224
Closed Access | Times Cited: 3
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