Inés Couso

3.3k total citations
111 papers, 2.1k citations indexed

About

Inés Couso is a scholar working on Artificial Intelligence, Management Science and Operations Research and Statistics and Probability. According to data from OpenAlex, Inés Couso has authored 111 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Artificial Intelligence, 54 papers in Management Science and Operations Research and 47 papers in Statistics and Probability. Recurrent topics in Inés Couso's work include Multi-Criteria Decision Making (53 papers), Fuzzy Systems and Optimization (44 papers) and Fuzzy Logic and Control Systems (43 papers). Inés Couso is often cited by papers focused on Multi-Criteria Decision Making (53 papers), Fuzzy Systems and Optimization (44 papers) and Fuzzy Logic and Control Systems (43 papers). Inés Couso collaborates with scholars based in Spain, France and Germany. Inés Couso's co-authors include Luciano Sánchez, Didier Dubois, Serafı́n Moral, Pedro Gil, Peter Walley, Enrique Miranda, Ana M. Palacios, Susana Montes, Jorge Casillas and José Otero and has published in prestigious journals such as IEEE Access, Sensors and Information Sciences.

In The Last Decade

Inés Couso

106 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Inés Couso Spain 26 1.2k 910 791 436 210 111 2.1k
José Sanz Spain 30 1.2k 1.0× 1.4k 1.5× 701 0.9× 967 2.2× 245 1.2× 78 2.7k
Rafik Aziz Aliev Azerbaijan 26 773 0.6× 995 1.1× 518 0.7× 249 0.6× 457 2.2× 74 1.9k
Yafei Song China 26 1.1k 0.9× 963 1.1× 313 0.4× 430 1.0× 330 1.6× 138 2.2k
Serafı́n Moral Spain 28 1.7k 1.3× 843 0.9× 399 0.5× 520 1.2× 116 0.6× 112 2.2k
Jonathan Lawry United Kingdom 23 875 0.7× 693 0.8× 238 0.3× 608 1.4× 110 0.5× 113 1.6k
Wenyi Zeng China 19 643 0.5× 1.4k 1.5× 724 0.9× 503 1.2× 356 1.7× 65 1.7k
Anne-Laure Jousselme Canada 16 1.1k 0.9× 746 0.8× 134 0.2× 335 0.8× 178 0.8× 83 1.9k
J.F. Baldwin United Kingdom 22 1.2k 0.9× 875 1.0× 316 0.4× 613 1.4× 247 1.2× 74 1.8k
Toshiaki Murofushi Japan 17 573 0.5× 1.2k 1.3× 799 1.0× 399 0.9× 346 1.6× 55 1.9k
Sanjay Kumar India 27 576 0.5× 1.2k 1.3× 263 0.3× 233 0.5× 291 1.4× 80 1.9k

Countries citing papers authored by Inés Couso

Since Specialization
Citations

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

Fields of papers citing papers by Inés Couso

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Inés Couso

This figure shows the co-authorship network connecting the top 25 collaborators of Inés Couso. A scholar is included among the top collaborators of Inés Couso 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 Inés Couso. Inés Couso 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.
Sánchez, Luciano, et al.. (2025). Self-supervised spectral learning with an application to condition monitoring of tunnel ventilation systems under sparse data conditions. Computers & Industrial Engineering. 211. 111634–111634.
2.
Sánchez, Luciano, et al.. (2025). Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks. Reliability Engineering & System Safety. 261. 111135–111135. 1 indexed citations
3.
Sánchez, Luciano, et al.. (2025). Data imputation in the frequency domain using Echo State Networks. Engineering Applications of Artificial Intelligence. 144. 110129–110129. 1 indexed citations
4.
Sánchez, Luciano, et al.. (2024). Integrating imprecise data in generative models using interval-valued Variational Autoencoders. Information Fusion. 114. 102659–102659. 2 indexed citations
5.
Couso, Inés & Didier Dubois. (2017). A general framework for maximizing likelihood under incomplete data. International Journal of Approximate Reasoning. 93. 238–260. 13 indexed citations
6.
Sánchez, Luciano & Inés Couso. (2017). A framework for learning fuzzy rule-based models with epistemic set-valued data and generalized loss functions. International Journal of Approximate Reasoning. 92. 321–339. 3 indexed citations
7.
Couso, Inés & Didier Dubois. (2015). A perspective on the extension of stochastic orderings to fuzzy random variables. Advances in intelligent systems research. 89. 1 indexed citations
8.
Destercke, Sébastien & Inés Couso. (2014). Ranking of fuzzy intervals seen through the imprecise probabilistic lens. Fuzzy Sets and Systems. 278. 20–39. 22 indexed citations
9.
Couso, Inés & Didier Dubois. (2014). Rejoinder on “Statistical reasoning with set-valued information: Ontic vs. epistemic views”. International Journal of Approximate Reasoning. 55(7). 1606–1608. 1 indexed citations
10.
Couso, Inés, et al.. (2014). Generalizing the Wilcoxon rank-sum test for interval data. International Journal of Approximate Reasoning. 56. 108–121. 63 indexed citations
11.
Couso, Inés & Luciano Sánchez. (2014). Harnessing the information contained in low-quality data sources. International Journal of Approximate Reasoning. 55(7). 1485–1486. 4 indexed citations
12.
Palacios, Ana M., Luciano Sánchez, & Inés Couso. (2011). Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data. International Journal of Approximate Reasoning. 52(6). 841–862. 16 indexed citations
13.
Couso, Inés & Serafı́n Moral. (2011). Sets of desirable gambles: Conditioning, representation, and precise probabilities. International Journal of Approximate Reasoning. 52(7). 1034–1055. 30 indexed citations
14.
Palacios, Ana M., Luciano Sánchez, & Inés Couso. (2009). A baseline learning genetic fuzzy classifier based on low quality data. Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte). 803–808. 1 indexed citations
15.
Sánchez, Luciano, et al.. (2008). Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data. International Journal of Approximate Reasoning. 49(3). 607–622. 33 indexed citations
16.
Sánchez, Luciano, et al.. (2007). Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data. Proceedings of ... IEEE International Conference on Fuzzy Systems. 1–6. 8 indexed citations
17.
Baudrit, Cédric, Inés Couso, & Didier Dubois. (2006). Joint propagation of probability and possibility in risk analysis: Towards a formal framework. International Journal of Approximate Reasoning. 45(1). 82–105. 72 indexed citations
18.
Miranda, Enrique, Inés Couso, & Pedro Gil. (2003). Study of the Probabilistic Information of a Random Set.. 382–394. 8 indexed citations
19.
Montes, Susana, Inés Couso, Pedro Gil, & Carlo Bertoluzza. (2002). Divergence measure between fuzzy sets. International Journal of Approximate Reasoning. 30(2). 91–105. 93 indexed citations
20.
Couso, Inés, Serafı́n Moral, & Peter Walley. (1999). Examples of Independence for Imprecise Probabilities. 121–130. 59 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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