George D. C. Cavalcanti

4.4k total citations · 1 hit paper
186 papers, 2.9k citations indexed

About

George D. C. Cavalcanti is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, George D. C. Cavalcanti has authored 186 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 99 papers in Artificial Intelligence, 81 papers in Computer Vision and Pattern Recognition and 29 papers in Signal Processing. Recurrent topics in George D. C. Cavalcanti's work include Machine Learning and Data Classification (43 papers), Face and Expression Recognition (35 papers) and Imbalanced Data Classification Techniques (29 papers). George D. C. Cavalcanti is often cited by papers focused on Machine Learning and Data Classification (43 papers), Face and Expression Recognition (35 papers) and Imbalanced Data Classification Techniques (29 papers). George D. C. Cavalcanti collaborates with scholars based in Brazil, Canada and Belgium. George D. C. Cavalcanti's co-authors include Rafael M. O. Cruz, Robert Sabourin, Tsang Ing Ren, Lucas Amorim, Paulo S. G. de Mattos Neto, Luiz S. Oliveira, Steven J. Simske, Fred Freitas, Rafael Dueire Lins and Francisco Madeiro and has published in prestigious journals such as PLoS ONE, Scientific Reports and Expert Systems with Applications.

In The Last Decade

George D. C. Cavalcanti

169 papers receiving 2.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The choice of scaling technique matters for classification performance

2022 149 citations Lucas Amorim, George D. C. Cavalcanti et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
George D. C. Cavalcanti Brazil	28	1.7k	790	345	299	282	186	2.9k
Juan J. Rodríguez Spain	25	1.8k 1.1×	699 0.9×	330 1.0×	393 1.3×	281 1.0×	76	3.3k
Tom Dietterich United States	11	2.1k 1.2×	890 1.1×	225 0.7×	238 0.8×	146 0.5×	20	3.3k
Qianli Ma China	26	1.4k 0.8×	757 1.0×	201 0.6×	496 1.7×	370 1.3×	101	3.2k
Gavin Brown United Kingdom	21	1.5k 0.9×	733 0.9×	271 0.8×	228 0.8×	252 0.9×	65	2.7k
Isaac Triguero Spain	30	2.2k 1.3×	772 1.0×	525 1.5×	562 1.9×	363 1.3×	90	3.5k
Tony Martinez United States	25	2.5k 1.5×	890 1.1×	492 1.4×	317 1.1×	228 0.8×	127	3.8k
Mourad Oussalah Finland	28	1.1k 0.7×	506 0.6×	309 0.9×	161 0.5×	181 0.6×	203	2.3k
Ran Wang China	28	1.5k 0.9×	614 0.8×	270 0.8×	200 0.7×	204 0.7×	88	2.4k
Bianca Zadrozny United States	22	2.9k 1.7×	643 0.8×	561 1.6×	220 0.7×	171 0.6×	56	3.9k

Countries citing papers authored by George D. C. Cavalcanti

Since Specialization

Citations

This map shows the geographic impact of George D. C. Cavalcanti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by George D. C. Cavalcanti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites George D. C. Cavalcanti more than expected).

Fields of papers citing papers by George D. C. Cavalcanti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by George D. C. Cavalcanti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by George D. C. Cavalcanti. The network helps show where George D. C. Cavalcanti may publish in the future.

Co-authorship network of co-authors of George D. C. Cavalcanti

This figure shows the co-authorship network connecting the top 25 collaborators of George D. C. Cavalcanti. A scholar is included among the top collaborators of George D. C. Cavalcanti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with George D. C. Cavalcanti. George D. C. Cavalcanti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Amorim, Lucas, et al.. (2025). PIPES: A Meta-dataset of Machine Learning Pipelines. Espace ÉTS (ETS). 1–8.

2.

Rosa, Nelson Souto, et al.. (2025). Univariate vs multivariate prediction for containerised applications auto-scaling: a comparative study. 1098–1105.

3.

Cavalcanti, George D. C., et al.. (2024). Gender bias detection on hate speech classification: an analysis at feature-level. Neural Computing and Applications. 37(5). 3887–3905.

4.

Sabourin, Robert, et al.. (2023). A dynamic multiple classifier system using graph neural network for high dimensional overlapped data. Information Fusion. 103. 102145–102145. 8 indexed citations

5.

Cavalcanti, George D. C., et al.. (2023). Dynamic Ensemble Algorithm Post-Selection Using Hardness-Aware Oracle. IEEE Access. 11. 86056–86070. 6 indexed citations

6.

Cavalcanti, George D. C., et al.. (2023). A Post-Selection Algorithm for Improving Dynamic Ensemble Selection Methods. Espace ÉTS (ETS). 1142–1147.

7.

Oliveira, Luiz S., et al.. (2023). Fault distance estimation for transmission lines with dynamic regressor selection. Neural Computing and Applications. 36(4). 1741–1759. 1 indexed citations

8.

Costa, Yandre M. G., Lucas Teixeira, Rodolfo M. Pereira, et al.. (2022). COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. Sensors. 22(19). 7303–7303. 9 indexed citations

9.

Neto, Paulo S. G. de Mattos, et al.. (2021). A Dynamic Predictor Selection Method Based on Recent Temporal Windows for Time Series Forecasting. IEEE Access. 9. 108466–108479. 8 indexed citations

10.

Cruz, Rafael M. O., Luiz G. Hafemann, Robert Sabourin, & George D. C. Cavalcanti. (2020). DESlib: A Dynamic ensemble selection library in Python. Espace ÉTS (ETS). 21(8). 1–5. 43 indexed citations

11.

Cavalcanti, George D. C., et al.. (2020). On the evaluation of dynamic selection parameters for time series forecasting. 1–7. 3 indexed citations

12.

Cruz, Rafael M. O., Robert Sabourin, & George D. C. Cavalcanti. (2017). META-DES.Oracle. arXiv (Cornell University). 38. 84–103. 36 indexed citations

13.

Janssens, Eline, George D. C. Cavalcanti, Tsang Ing Ren, et al.. (2017). Inline discrete tomography system: Application to agricultural product inspection. Lirias (KU Leuven). 20 indexed citations

14.

Cavalcanti, George D. C., et al.. (2015). A bootstrap-based iterative selection for ensemble generation. 1–7. 3 indexed citations

15.

Cavalcanti, George D. C., et al.. (2014). A Proposal for Path Loss Prediction in Urban Environments using Support Vector Regression. International Conference on Telecommunications. 119–124. 39 indexed citations

16.

Cavalcanti, George D. C., et al.. (2013). ATISA: Adaptive Threshold-based Instance Selection Algorithm. Expert Systems with Applications. 40(17). 6894–6900. 20 indexed citations

17.

Ren, Tsang Ing, et al.. (2012). Pupil segmentation using Pulling & Pushing and BSOM neural network. 4. 2359–2364. 3 indexed citations

18.

Vasconcelos, Germano C., et al.. (2011). BSOM network for pupil segmentation. 2704–2709. 4 indexed citations

19.

Cavalcanti, George D. C., et al.. (2007). Hybrid Solution for the Feature Selection in Personal Identification Problems through Keystroke Dynamics. IEEE International Conference on Neural Networks. 1947–1952. 14 indexed citations

20.

Cavalcanti, George D. C., et al.. (2004). Eigenbands fusion for frontal face recognition. 1. I–665. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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