César García‐Osorio

1.9k citations
56 papers · 1.3k indexed · h-index 22

César García‐Osorio

54 papers receiving 1.3k citations

Peers

César García‐Osorio
Comparison fields: 5 of 141
  • Artificial Intelligence 867
  • Health Information Management 91
  • Computer Science Applications 89
  • Computer Vision and Pattern Recognition 208
  • Health Informatics 11
Replace José-Francisco Díez-Pastor with:
José-Francisco Díez-Pastor Spain
Michael G. Madden Ireland
Sabah Mohammed Canada
Ahmad S. Tarawneh Jordan
Debo Cheng China
Qingguo Zhou China
Felix Last Germany
Mohammad‐Reza Feizi‐Derakhshi Iran
Horst Samulowitz United States
Chongsheng Zhang China
César García‐Osorio relative to José-Francisco Díez-Pastor Spain José-Francisco Díez-Pastor's profile →
Citations per field
00.5×1.5×2.2×
José-Francisco Díez-Pastor · 1×
Citations per year

Countries citing papers authored by César García‐Osorio

Since Specialization
Citations

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

Fields of papers citing papers by César García‐Osorio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by César García‐Osorio. 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 César García‐Osorio. The network helps show where César García‐Osorio may publish in the future.

Co-authorship network

The 25 scholars most cited alongside César García‐Osorio, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with César García‐Osorio Line = papers co-authored together César García‐Osorio links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20250
3 20241
4 20231
5 20231
6 20222
7 202027
8 202026
9 201923
10 201978
11 20197
12 201624
13 2015154
14 201070
15 200913
16
Constructing ensembles of classifiers using linear projections based on misclassified instances
20081
17
Nonlinear Boosting Projections for Ensemble Construction
200771
18 200523
19 20054
20
Using Andrews Curves for Clustering and Sub-clustering Self-Organizing Maps
20042

About César García‐Osorio

César García‐Osorio is a scholar working on Artificial Intelligence, Computer Science Applications and Information Systems, having authored 56 papers that have together received 1.3k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (20 papers), Imbalanced Data Classification Techniques (14 papers), Neural Networks and Applications (12 papers), Data Mining Algorithms and Applications (9 papers), Face and Expression Recognition (6 papers), Machine Learning and Algorithms (5 papers), Data Stream Mining Techniques (4 papers) and Online Learning and Analytics (3 papers). The work is most often cited by research in Artificial Intelligence (867 citations), Health Information Management (91 citations) and Computer Science Applications (89 citations). César García‐Osorio has collaborated with scholars based in Spain, United Kingdom and Poland. Frequent co-authors include Juan J. Rodríguez, José-Francisco Díez-Pastor, Nicolás García‐Pedrajas, Ludmila I. Kuncheva, Álvar Arnaiz‐González, Colin Fyfe, Jesús Maudes, Luke Sy, Aida de Haro-García and María Consuelo Sáiz Manzanares. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and BMC Bioinformatics.

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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