Sergio Ramírez‐Gallego
- Artificial Intelligence top 1%
- Information Systems top 2%
- Computer Networks and Communications top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Co-authors
- Francisco HerreraSalvador GarcíaJosé M. BenítezJulián LuengoBartosz KrawczykMichał WoźniakDavid Martínez‐RegoVerónica Bolón‐Canedo
- Topics
- Machine Learning and Data Classification (14 papers)Data Stream Mining Techniques (8 papers)Data Mining Algorithms and Applications (5 papers)
- Partner nations
- SpainSaudi ArabiaUnited Kingdom
In The Last Decade
Sergio Ramírez‐Gallego
21 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 135
- Artificial Intelligence 933
- Information Systems 363
- Computer Networks and Communications 224
- Computer Vision and Pattern Recognition 216
- Signal Processing 168
Countries citing papers authored by Sergio Ramírez‐Gallego
This map shows the geographic impact of Sergio Ramírez‐Gallego'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 Sergio Ramírez‐Gallego with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergio Ramírez‐Gallego more than expected).
Fields of papers citing papers by Sergio Ramírez‐Gallego
This network shows the impact of papers produced by Sergio Ramírez‐Gallego. 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 Sergio Ramírez‐Gallego. The network helps show where Sergio Ramírez‐Gallego may publish in the future.
Co-authorship network of co-authors of Sergio Ramírez‐Gallego
This figure shows the co-authorship network connecting the top 25 collaborators of Sergio Ramírez‐Gallego. A scholar is included among the top collaborators of Sergio Ramírez‐Gallego 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 Sergio Ramírez‐Gallego. Sergio Ramírez‐Gallego is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 17 | |
| 3 | 64 | |
| 4 | 30 | |
| 5 | 12 | |
| 6 | 59 | |
| 7 | A survey on data preprocessing for data stream mining: Current status and future directionsbreakdown → | 312 |
| 8 | 98 | |
| 9 | 61 | |
| 10 | 25 | |
| 11 | 1 | |
| 12 | 37 | |
| 13 | Big data preprocessing: methods and prospectsbreakdown → | 399 |
| 14 | 133 | |
| 15 | 14 | |
| 16 | 111 | |
| 17 | 102 | |
| 18 | 38 | |
| 19 | 2 | |
| 20 | 88 |
About Sergio Ramírez‐Gallego
Sergio Ramírez‐Gallego is a scholar working on Artificial Intelligence, Information Systems and Management Information Systems, having authored 21 papers that have together received 1.7k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (14 papers), Data Stream Mining Techniques (8 papers) and Data Mining Algorithms and Applications (5 papers). The work is most often cited by research in Artificial Intelligence (933 citations), Health Information Management (104 citations) and Information Systems (363 citations). Sergio Ramírez‐Gallego has collaborated with scholars based in Spain, Saudi Arabia and United Kingdom. Frequent co-authors include Francisco Herrera, Salvador García, José M. Benítez, Julián Luengo, Bartosz Krawczyk, Michał Woźniak, David Martínez‐Rego, Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos and Diego García‐Gil. Their work appears in journals such as Expert Systems with Applications, Information Sciences and IEEE Transactions on Cybernetics.
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.