Philippe Smets

9.7k total citations · 1 hit paper
54 papers, 4.3k citations indexed

About

Philippe Smets is a scholar working on Artificial Intelligence, Management Science and Operations Research and Statistics and Probability. According to data from OpenAlex, Philippe Smets has authored 54 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 29 papers in Management Science and Operations Research and 7 papers in Statistics and Probability. Recurrent topics in Philippe Smets's work include Bayesian Modeling and Causal Inference (35 papers), Multi-Criteria Decision Making (29 papers) and Logic, Reasoning, and Knowledge (20 papers). Philippe Smets is often cited by papers focused on Bayesian Modeling and Causal Inference (35 papers), Multi-Criteria Decision Making (29 papers) and Logic, Reasoning, and Knowledge (20 papers). Philippe Smets collaborates with scholars based in Belgium, France and Australia. Philippe Smets's co-authors include Robert Kennes, Paul Magrez, Branko Ristić, Thierry Denœux, Khaled Mellouli, Amihai Motro, Kathleen L. Henebry, Zied Elouedi, Dov M. Gabbay and P Rocmans and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Information Sciences and Artificial Intelligence.

In The Last Decade

Philippe Smets

50 papers receiving 4.0k citations

Hit Papers

The transferable belief model 1994 2026 2004 2015 1994 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Philippe Smets Belgium 29 2.7k 1.8k 871 476 404 54 4.3k
Jean Dezert France 31 1.9k 0.7× 1.1k 0.6× 723 0.8× 268 0.6× 253 0.6× 151 3.2k
Henri Prade France 26 2.1k 0.8× 1.3k 0.8× 1.0k 1.2× 516 1.1× 283 0.7× 99 3.2k
Rudolf Kruse Germany 35 3.6k 1.3× 1.2k 0.7× 861 1.0× 897 1.9× 252 0.6× 224 6.0k
Thierry Denœux France 44 4.0k 1.5× 1.7k 1.0× 1.2k 1.4× 786 1.7× 341 0.8× 180 6.6k
Wen Jiang China 42 2.0k 0.7× 2.3k 1.3× 908 1.0× 528 1.1× 247 0.6× 181 5.1k
Didier Dubois France 13 2.0k 0.7× 2.8k 1.6× 1.1k 1.3× 1.6k 3.4× 231 0.6× 34 5.1k
Henri Prade France 2 1.8k 0.7× 2.6k 1.5× 989 1.1× 1.5k 3.2× 203 0.5× 2 4.7k
Éloi Bossé Canada 24 1.4k 0.5× 732 0.4× 333 0.4× 139 0.3× 378 0.9× 118 2.5k
Edurne Barrenechea Spain 35 3.6k 1.3× 1.9k 1.1× 1.3k 1.4× 868 1.8× 198 0.5× 91 6.4k
Siegfried Gottwald Germany 19 1.5k 0.6× 1.5k 0.8× 1.5k 1.7× 650 1.4× 142 0.4× 71 3.1k

Countries citing papers authored by Philippe Smets

Since Specialization
Citations

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

Fields of papers citing papers by Philippe Smets

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philippe Smets

This figure shows the co-authorship network connecting the top 25 collaborators of Philippe Smets. A scholar is included among the top collaborators of Philippe Smets 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 Philippe Smets. Philippe Smets 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.
Smets, Philippe. (2005). Belief functions on real numbers. International Journal of Approximate Reasoning. 40(3). 181–223. 98 indexed citations
2.
Yaghlane, Boutheina Ben, Philippe Smets, & Khaled Mellouli. (2002). Belief function independence: I. The marginal case. International Journal of Approximate Reasoning. 29(1). 47–70. 33 indexed citations
3.
Yaghlane, Boutheina Ben, Philippe Smets, & Khaled Mellouli. (2002). Belief function independence: II. The conditional case. International Journal of Approximate Reasoning. 31(1-2). 31–75. 29 indexed citations
4.
Elouedi, Zied, Khaled Mellouli, & Philippe Smets. (2001). Belief decision trees: theoretical foundations. International Journal of Approximate Reasoning. 28(2-3). 91–124. 80 indexed citations
5.
Dubois, Didier, Henri Prade, & Philippe Smets. (2001). New semantics for quantitative possibility theory.. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 152–161. 3 indexed citations
6.
Smets, Philippe. (1998). Application of the transferable belief model to diagnostic problems. International Journal of Intelligent Systems. 13(2-3). 127–157. 47 indexed citations
7.
Besnard, Philippe, Anthony Hunter, Dov M. Gabbay, & Philippe Smets. (1998). Handbook of defeasible reasoning and uncertainty management systems: volume 2: reasoning with actual and potential contradictions. Kluwer Academic Publishers eBooks. 11 indexed citations
8.
Smets, Philippe. (1997). The normative representation of quantified beliefs by belief functions. Artificial Intelligence. 92(1-2). 229–242. 31 indexed citations
9.
Smets, Philippe. (1995). The canonical decomposition of a weighted belief. International Joint Conference on Artificial Intelligence. 42(2). 1896–1901. 86 indexed citations
10.
Smets, Philippe. (1994). What is Dempster-Shafer's model?. John Wiley & Sons, Inc. eBooks. 5–34. 83 indexed citations
11.
Smets, Philippe. (1994). About updating. Uncertainty in Artificial Intelligence. 378–385. 23 indexed citations
12.
Smets, Philippe. (1993). Probability of deductibility and belief functions. Lecture notes in computer science. 332–340.
13.
Smets, Philippe. (1993). Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning. 9(1). 1–35. 367 indexed citations
14.
Smets, Philippe. (1992). Resolving misunderstandings about belief functions. International Journal of Approximate Reasoning. 6(3). 321–344. 26 indexed citations
15.
Smets, Philippe. (1991). Quantifying Beliefs by Belief Functions: An Axiomatic Justification. International Joint Conference on Artificial Intelligence. 598–603. 10 indexed citations
16.
Kennes, Robert & Philippe Smets. (1990). Computational aspects of the Mobius transformation. arXiv (Cornell University). 401–416. 28 indexed citations
17.
Smets, Philippe. (1988). Transferable belief model versus bayesian model. European Conference on Artificial Intelligence. 495–500. 3 indexed citations
18.
Clark, Dominic A., James Mark Baldwin, H.R. Berenji, et al.. (1988). Responses to “An AI view of the treatment of uncertainty” by Alessandro Saffiotti. The Knowledge Engineering Review. 3(1). 59–86. 5 indexed citations
19.
Smets, Philippe & Paul Magrez. (1987). Implication in fuzzy logic. International Journal of Approximate Reasoning. 1(4). 327–347. 160 indexed citations
20.
Levi, Salvator, et al.. (1982). RAPPORT EFFICACITE-COUT DE L'ULTRASONOGRAPHIE OBSTETRICALE. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 11(6). 665–675. 1 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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