Ricardo Cerri

1.6k total citations
69 papers, 844 citations indexed

About

Ricardo Cerri is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ricardo Cerri has authored 69 papers receiving a total of 844 indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 30 papers in Molecular Biology and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ricardo Cerri's work include Text and Document Classification Technologies (33 papers), Machine Learning in Bioinformatics (20 papers) and Machine Learning and Data Classification (17 papers). Ricardo Cerri is often cited by papers focused on Text and Document Classification Technologies (33 papers), Machine Learning in Bioinformatics (20 papers) and Machine Learning and Data Classification (17 papers). Ricardo Cerri collaborates with scholars based in Brazil, United Kingdom and Belgium. Ricardo Cerri's co-authors include Rodrigo C. Barros, André C. P. L. F. de Carvalho, Jônatas Wehrmann, Rafael Gomes Mantovani, Sylvio Barbon, Yaochu Jin, Joaquin Vanschoren, Tomáš Horváth, Alex A. Freitas and Saulo Martiello Mastelini and has published in prestigious journals such as Bioinformatics, Expert Systems with Applications and Gene.

In The Last Decade

Ricardo Cerri

62 papers receiving 824 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ricardo Cerri Brazil 16 494 225 148 120 87 69 844
Alneu de Andrade Lopes Brazil 16 419 0.8× 95 0.4× 199 1.3× 117 1.0× 39 0.4× 72 808
A. Fazel Famili Canada 10 219 0.4× 132 0.6× 58 0.4× 97 0.8× 66 0.8× 22 625
Laura Elena Raileanu Switzerland 6 211 0.4× 77 0.3× 107 0.7× 55 0.5× 35 0.4× 12 570
Ibrahim El-Henawy Egypt 11 394 0.8× 44 0.2× 107 0.7× 70 0.6× 80 0.9× 24 631
Mark Junjie Li China 11 301 0.6× 108 0.5× 148 1.0× 79 0.7× 47 0.5× 26 596
Shyr-Shen Yu Taiwan 13 326 0.7× 98 0.4× 280 1.9× 32 0.3× 136 1.6× 79 715
Prabhas Chongstitvatana Thailand 12 358 0.7× 64 0.3× 68 0.5× 97 0.8× 123 1.4× 103 707
Albert Orriols-Puig Spain 15 688 1.4× 101 0.4× 126 0.9× 85 0.7× 75 0.9× 33 874
Avinash Chandra Pandey India 15 508 1.0× 37 0.2× 198 1.3× 145 1.2× 35 0.4× 32 794
Andrea Bommert Germany 4 251 0.5× 121 0.5× 97 0.7× 51 0.4× 32 0.4× 8 583

Countries citing papers authored by Ricardo Cerri

Since Specialization
Citations

This map shows the geographic impact of Ricardo Cerri'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 Ricardo Cerri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ricardo Cerri more than expected).

Fields of papers citing papers by Ricardo Cerri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ricardo Cerri. 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 Ricardo Cerri. The network helps show where Ricardo Cerri may publish in the future.

Co-authorship network of co-authors of Ricardo Cerri

This figure shows the co-authorship network connecting the top 25 collaborators of Ricardo Cerri. A scholar is included among the top collaborators of Ricardo Cerri 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 Ricardo Cerri. Ricardo Cerri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cao, Yu, Renato Silva, Helena de Medeiros Caseli, et al.. (2025). Automated Machine Learning in medical research: A systematic literature mapping study. Artificial Intelligence in Medicine. 171. 103302–103302.
2.
Cerri, Ricardo, et al.. (2024). Deep forests with tree-embeddings and label imputation for weak-label learning. Lirias (KU Leuven). 1–8.
3.
Cerri, Ricardo, et al.. (2024). Multi-label classification with label clusters. Knowledge and Information Systems. 67(2). 1741–1785.
4.
Faria, Elaine R., et al.. (2023). Novelty detection for multi-label stream classification under extreme verification latency. Applied Soft Computing. 141. 110265–110265. 1 indexed citations
5.
6.
Cerri, Ricardo, et al.. (2023). Multi-label classification via closed frequent labelsets and label taxonomies. Soft Computing. 27(13). 8627–8660. 4 indexed citations
7.
Mantovani, Rafael Gomes, et al.. (2023). A systematic literature review on AutoML for multi-target learning tasks. Artificial Intelligence Review. 56(S2). 2013–2052. 7 indexed citations
8.
9.
Christinelli, Wania, Flávio M. Shimizu, Murilo H. M. Facure, et al.. (2021). Two-dimensional MoS2-based impedimetric electronic tongue for the discrimination of endocrine disrupting chemicals using machine learning. Sensors and Actuators B Chemical. 336. 129696–129696. 23 indexed citations
10.
Faria, Elaine R., et al.. (2019). Novelty Detection for Multi-Label Stream Classification. 144–149. 6 indexed citations
11.
Wehrmann, Jônatas, Rodrigo C. Barros, & Ricardo Cerri. (2018). Hierarchical Multi-Label Classification Networks. International Conference on Machine Learning. 5075–5084. 80 indexed citations
12.
Lorena, Ana Carolina, et al.. (2018). Can I make a wish?: a competition on detecting meteors in images. 89–96. 1 indexed citations
13.
Cerri, Ricardo, et al.. (2018). Hierarchical and Non-Hierarchical Classification of Transposable Elements with a Genetic Algorithm. Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais). 9(2). 163–163. 2 indexed citations
14.
Schietgat, Leander, Celine Vens, Ricardo Cerri, et al.. (2018). A machine learning based framework to identify and classify long terminal repeat retrotransposons. PLoS Computational Biology. 14(4). e1006097–e1006097. 24 indexed citations
15.
Cerri, Ricardo, et al.. (2016). Hyper-Parameter Tuning of a Decision Tree Induction Algorithm. IEEE Conference Proceedings. 2016. 42. 1 indexed citations
16.
Triguero, Isaac, Márcio P. Basgalupp, Ricardo Cerri, Leander Schietgat, & Celine Vens. (2016). Partitioning the target space in multi-output learning. Lirias (KU Leuven). 1 indexed citations
17.
Schietgat, Leander, et al.. (2013). Annotating transposable elements in the genome using relational decision tree ensembles. Lirias (KU Leuven). 1–6. 3 indexed citations
18.
Cerri, Ricardo, Rodrigo C. Barros, & André C. P. L. F. de Carvalho. (2013). Hierarchical multi-label classification using local neural networks. Journal of Computer and System Sciences. 80(1). 39–56. 85 indexed citations
19.
Cerri, Ricardo, Gisele L. Pappa, André C. P. L. F. de Carvalho, & Alex A. Freitas. (2013). An Extensive Evaluation of Decision Tree–Based Hierarchical Multilabel Classification Methods and Performance Measures. Computational Intelligence. 31(1). 1–46. 22 indexed citations
20.
Cerri, Ricardo, Rodrigo C. Barros, & André C. P. L. F. de Carvalho. (2012). A genetic algorithm for Hierarchical Multi-Label Classification. 250–255. 29 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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