José C. Riquelme
- Software top 2%
- Artificial Intelligence top 1%
- Evolutionary Algorithms and Applications 14
- Metaheuristic Optimization Algorithms Research 14
- Data Stream Mining Techniques 12
- Machine Learning and Data Classification 11
- Information Systems top 1%
- Data Mining Algorithms and Applications 28
- Signal Processing top 5%
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- Rough Sets and Fuzzy Logic 14
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- Energy Load and Power Forecasting 13
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- Gene expression and cancer classification 12
- Co-authors
- Jesús S. Aguilar–RuizAlicia TroncosoFrancisco Martínez‐ÁlvarezJorge García–GutiérrezRoberto RuízManuel Carranza-GarcíaJosé María Luna-RomeraM. Martínez-Ballesteros
- Journals
- Applied Energy (1 paper)European Journal of Operational Research (1 paper)IEEE Transactions on Power Systems (1 paper)
- Partner nations
- SpainUnited KingdomChile
In The Last Decade
José C. Riquelme
123 papers receiving 2.9k citations
Peers
Comparison fields: 5 of 153
- Software 201
- Artificial Intelligence 1.2k
- Information Systems 584
- Management Science and Operations Research 318
- Signal Processing 247
Countries citing papers authored by José C. Riquelme
This map shows the geographic impact of José C. Riquelme'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 José C. Riquelme with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José C. Riquelme more than expected).
Fields of papers citing papers by José C. Riquelme
This network shows the impact of papers produced by José C. Riquelme. 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 José C. Riquelme. The network helps show where José C. Riquelme may publish in the future.
Co-authorship network
The 25 scholars most cited alongside José C. Riquelme, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 9 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 3 | |
| 6 | 2023 | 3 | |
| 7 | 2022 | 1 | |
| 8 | 2019 | 28 | |
| 9 | 2018 | 74 | |
| 10 | 2018 | 29 | |
| 11 | 2017 | 18 | |
| 12 | 2015 | 2 | |
| 13 | 2013 | 19 | |
| 14 | 2010 | 1 | |
| 15 | JISBD 04: Finding Defective Software Modules by Means of Data Mining Techniques | 2009 | 2 |
| 16 | Minería de Datos: Conceptos y Tendencias | 2006 | 15 |
| 17 | Improving the evolutionary coding for machine learning tasks | 2002 | 7 |
| 18 | 2001 | 1 | |
| 19 | Discovering hierarchical decision rules with evolutive algorithms in supervised learning | 2000 | 8 |
| 20 | Three geometric approaches for representing decision rules in a supervised learning system | 1999 | 5 |
About José C. Riquelme
José C. Riquelme is a scholar working on Software, Artificial Intelligence and Information Systems, having authored 127 papers that have together received 3.0k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (28 papers), Evolutionary Algorithms and Applications (14 papers), Rough Sets and Fuzzy Logic (14 papers), Metaheuristic Optimization Algorithms Research (14 papers), Energy Load and Power Forecasting (13 papers), Gene expression and cancer classification (12 papers), Data Stream Mining Techniques (12 papers) and Machine Learning and Data Classification (11 papers). The work is most often cited by research in Software (201 citations), Artificial Intelligence (1.2k citations) and Information Systems (584 citations). José C. Riquelme has collaborated with scholars based in Spain, United Kingdom and Chile. Frequent co-authors include Jesús S. Aguilar–Ruiz, Alicia Troncoso, Francisco Martínez‐Álvarez, Jorge García–Gutiérrez, Roberto Ruíz, Manuel Carranza-García, José María Luna-Romera, M. Martínez-Ballesteros, Miguel Toro and Pedro Lara-Benítez. Their work appears in journals such as Applied Energy, European Journal of Operational Research and IEEE Transactions on Power Systems.
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.