Gauthier Doquire

901 total citations
16 papers, 610 citations indexed

About

Gauthier Doquire is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics and Probability. According to data from OpenAlex, Gauthier Doquire has authored 16 papers receiving a total of 610 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 4 papers in Statistics and Probability. Recurrent topics in Gauthier Doquire's work include Face and Expression Recognition (11 papers), Neural Networks and Applications (8 papers) and Advanced Statistical Methods and Models (3 papers). Gauthier Doquire is often cited by papers focused on Face and Expression Recognition (11 papers), Neural Networks and Applications (8 papers) and Advanced Statistical Methods and Models (3 papers). Gauthier Doquire collaborates with scholars based in Belgium, Finland and Spain. Gauthier Doquire's co-authors include Michel Verleysen, Gaël de Lannoy, D. François, Benoît Frénay‬, Amaury Lendasse, Emil Eirola and Damien François and has published in prestigious journals such as Information Sciences, Neurocomputing and Neural Networks.

In The Last Decade

Gauthier Doquire

16 papers receiving 594 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gauthier Doquire Belgium 10 276 170 162 74 45 16 610
S.D. Katebi Iran 13 303 1.1× 58 0.3× 161 1.0× 63 0.9× 61 1.4× 31 629
André L. V. Coelho Brazil 14 306 1.1× 103 0.6× 224 1.4× 47 0.6× 35 0.8× 58 710
Changhe Yuan United States 12 274 1.0× 56 0.3× 97 0.6× 28 0.4× 37 0.8× 46 508
Jiashuang Huang China 18 313 1.1× 226 1.3× 437 2.7× 64 0.9× 40 0.9× 77 1.1k
Clodoaldo A. M. Lima Brazil 13 215 0.8× 121 0.7× 254 1.6× 36 0.5× 17 0.4× 51 653
Chen‐Sen Ouyang Taiwan 14 252 0.9× 127 0.7× 212 1.3× 14 0.2× 31 0.7× 70 670
Bharat Richhariya India 14 538 1.9× 420 2.5× 159 1.0× 63 0.9× 27 0.6× 19 992
R. Rajesh India 16 125 0.5× 250 1.5× 129 0.8× 25 0.3× 40 0.9× 88 823
Arif Gülten Türkiye 12 207 0.8× 195 1.1× 38 0.2× 54 0.7× 26 0.6× 36 606
Tobias Glasmachers Germany 16 406 1.5× 133 0.8× 99 0.6× 40 0.5× 8 0.2× 43 741

Countries citing papers authored by Gauthier Doquire

Since Specialization
Citations

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

Fields of papers citing papers by Gauthier Doquire

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gauthier Doquire

This figure shows the co-authorship network connecting the top 25 collaborators of Gauthier Doquire. A scholar is included among the top collaborators of Gauthier Doquire 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 Gauthier Doquire. Gauthier Doquire is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Doquire, Gauthier, Benoît Frénay‬, & Michel Verleysen. (2013). Risk Estimation and Feature Selection. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 1 indexed citations
2.
Doquire, Gauthier & Michel Verleysen. (2013). A graph Laplacian based approach to semi-supervised feature selection for regression problems. Neurocomputing. 121. 5–13. 63 indexed citations
3.
Frénay‬, Benoît, Gauthier Doquire, & Michel Verleysen. (2013). Is mutual information adequate for feature selection in regression?. Neural Networks. 48. 1–7. 62 indexed citations
4.
Frénay‬, Benoît, Gauthier Doquire, & Michel Verleysen. (2013). Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification. Neurocomputing. 112. 64–78. 24 indexed citations
5.
Eirola, Emil, Gauthier Doquire, Michel Verleysen, & Amaury Lendasse. (2013). Distance estimation in numerical data sets with missing values. Information Sciences. 240. 115–128. 37 indexed citations
6.
Frénay‬, Benoît, Gauthier Doquire, & Michel Verleysen. (2013). Estimating mutual information for feature selection in the presence of label noise. Computational Statistics & Data Analysis. 71. 832–848. 27 indexed citations
7.
Doquire, Gauthier & Michel Verleysen. (2013). Mutual information-based feature selection for multilabel classification. Neurocomputing. 122. 148–155. 120 indexed citations
8.
Frénay‬, Benoît, Gauthier Doquire, & Michel Verleysen. (2012). On the Potential Inadequacy of Mutual Information for Feature Selection. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 5 indexed citations
9.
Frénay‬, Benoît, Gauthier Doquire, & Michel Verleysen. (2012). On the Potential Inadequacy of Mutual Information for Feature Selection: Proceedings of the 20th International Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012). 2 indexed citations
10.
Doquire, Gauthier & Michel Verleysen. (2012). Feature selection with missing data using mutual information estimators. Neurocomputing. 90. 3–11. 55 indexed citations
11.
Doquire, Gauthier & Michel Verleysen. (2012). A COMPARISON OF MULTIVARIATE MUTUAL INFORMATION ESTIMATORS FOR FEATURE SELECTION. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 176–185. 25 indexed citations
12.
Doquire, Gauthier & Michel Verleysen. (2011). Mutual information based feature selection for mixed data. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 4 indexed citations
13.
Doquire, Gauthier, Gaël de Lannoy, Damien François, & Michel Verleysen. (2011). Feature selection for supervised inter-patient heart beat classification. Computational Intelligence and Neuroscience. 2011(643816). 1–9. 1 indexed citations
14.
Doquire, Gauthier & Michel Verleysen. (2011). Mutual information for feature selection with missing data. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 1 indexed citations
15.
Doquire, Gauthier & Michel Verleysen. (2011). AN HYBRID APPROACH TO FEATURE SELECTION FOR MIXED CATEGORICAL AND CONTINUOUS DATA. 394–401. 13 indexed citations
16.
Doquire, Gauthier, Gaël de Lannoy, D. François, & Michel Verleysen. (2011). Feature Selection for Interpatient Supervised Heart Beat Classification. Computational Intelligence and Neuroscience. 2011. 1–9. 170 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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