Rita P. Ribeiro
- Artificial Intelligence top 2%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Luı́s TorgoPaula BrancoNuno MonizJoão GamaPedro PereiraBernhard PfahringerBruno VelosoNarjes Davari
- Topics
- Imbalanced Data Classification Techniques (13 papers)Anomaly Detection Techniques and Applications (9 papers)Machine Learning and Data Classification (8 papers)
- Journals
- CancerSensorsACM Computing Surveys
- Partner nations
- PortugalUnited StatesNew Zealand
In The Last Decade
Rita P. Ribeiro
30 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 162
- Artificial Intelligence 679
- Electrical and Electronic Engineering 195
- Control and Systems Engineering 104
- Information Systems 96
- Computer Vision and Pattern Recognition 95
Countries citing papers authored by Rita P. Ribeiro
This map shows the geographic impact of Rita P. Ribeiro'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 Rita P. Ribeiro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rita P. Ribeiro more than expected).
Fields of papers citing papers by Rita P. Ribeiro
This network shows the impact of papers produced by Rita P. Ribeiro. 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 Rita P. Ribeiro. The network helps show where Rita P. Ribeiro may publish in the future.
Co-authorship network of co-authors of Rita P. Ribeiro
This figure shows the co-authorship network connecting the top 25 collaborators of Rita P. Ribeiro. A scholar is included among the top collaborators of Rita P. Ribeiro 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 Rita P. Ribeiro. Rita P. Ribeiro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 13 | |
| 7 | 6 | |
| 8 | 4 | |
| 9 | 19 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 24 | |
| 13 | 1 | |
| 14 | REBAGG: REsampled BAGGing for Imbalanced Regression | 11 |
| 15 | 4 | |
| 16 | SMOGN: a Pre-processing Approach for Imbalanced Regression | 90 |
| 17 | 3 | |
| 18 | A Survey of Predictive Modeling on Imbalanced Domainsbreakdown → | 738 |
| 19 | Innovative smart metering based applications for water utilities | 1 |
| 20 | 1 |
About Rita P. Ribeiro
Rita P. Ribeiro is a scholar working on Medical Laboratory Technology, Health Information Management and Artificial Intelligence, having authored 33 papers that have together received 1.3k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (13 papers), Anomaly Detection Techniques and Applications (9 papers) and Machine Learning and Data Classification (8 papers). The work is most often cited by research in Artificial Intelligence (679 citations), Medical Laboratory Technology (22 citations) and Health Information Management (65 citations). Rita P. Ribeiro has collaborated with scholars based in Portugal, United States and New Zealand. Frequent co-authors include Luı́s Torgo, Paula Branco, Nuno Moniz, João Gama, Pedro Pereira, Bernhard Pfahringer, Bruno Veloso, Narjes Davari, Nitesh V. Chawla and Vítor Cerqueira. Their work appears in journals such as Cancer, Sensors and ACM Computing Surveys.
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.