Stéphane Lallich

1.2k total citations
27 papers, 365 citations indexed

About

Stéphane Lallich is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, Stéphane Lallich has authored 27 papers receiving a total of 365 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 11 papers in Information Systems and 7 papers in Computational Theory and Mathematics. Recurrent topics in Stéphane Lallich's work include Data Mining Algorithms and Applications (11 papers), Imbalanced Data Classification Techniques (7 papers) and Rough Sets and Fuzzy Logic (7 papers). Stéphane Lallich is often cited by papers focused on Data Mining Algorithms and Applications (11 papers), Imbalanced Data Classification Techniques (7 papers) and Rough Sets and Fuzzy Logic (7 papers). Stéphane Lallich collaborates with scholars based in France, Czechia and Vietnam. Stéphane Lallich's co-authors include Philippe Lenca, Fabrice Muhlenbach, Benoît Vaillant, Djamel A. Zighed, Patrick Meyer, Marc Sebban, Richard Nock, Christian Le Bas, Sophie Bergeron and Thanh‐Nghi Do and has published in prestigious journals such as European Journal of Operational Research, Research Policy and Pattern Recognition.

In The Last Decade

Stéphane Lallich

25 papers receiving 337 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stéphane Lallich France 9 210 120 103 67 41 27 365
Paul Leng United Kingdom 11 275 1.3× 274 2.3× 186 1.8× 15 0.2× 72 1.8× 48 464
Ahmed Sharaf Eldin Egypt 11 153 0.7× 93 0.8× 59 0.6× 20 0.3× 10 0.2× 43 351
Bavani Arunasalam Australia 8 127 0.6× 88 0.7× 29 0.3× 14 0.2× 45 1.1× 9 304
Atsuyoshi Nakamura Japan 8 129 0.6× 152 1.3× 37 0.4× 48 0.7× 27 0.7× 47 384
Rob Potharst Netherlands 9 187 0.9× 90 0.8× 91 0.9× 25 0.4× 14 0.3× 18 307
Marcel Holsheimer Netherlands 7 144 0.7× 202 1.7× 88 0.9× 12 0.2× 76 1.9× 7 323
Adam G.M. Pazdor Canada 11 174 0.8× 142 1.2× 26 0.3× 45 0.7× 69 1.7× 35 317
Brij Masand United States 11 365 1.7× 330 2.8× 44 0.4× 67 1.0× 41 1.0× 22 595
Thiago Salles Brazil 12 338 1.6× 136 1.1× 24 0.2× 65 1.0× 20 0.5× 31 512
Cheng-Hsiung Weng Taiwan 9 124 0.6× 143 1.2× 71 0.7× 15 0.2× 48 1.2× 16 310

Countries citing papers authored by Stéphane Lallich

Since Specialization
Citations

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

Fields of papers citing papers by Stéphane Lallich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stéphane Lallich

This figure shows the co-authorship network connecting the top 25 collaborators of Stéphane Lallich. A scholar is included among the top collaborators of Stéphane Lallich 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 Stéphane Lallich. Stéphane Lallich 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.
Muhlenbach, Fabrice, et al.. (2015). Comparison of two topological approaches for dealing with noisy labeling. Neurocomputing. 160. 3–17. 1 indexed citations
2.
Rizoiu, Marian-Andrei, Julien Velcin, & Stéphane Lallich. (2015). Semantic-enriched visual vocabulary construction in a weakly supervised context. Intelligent Data Analysis. 19(1). 161–185. 1 indexed citations
3.
Metzger, Marie-Hélène, et al.. (2012). The use of regional platforms for managing electronic health records for the production of regional public health indicators in France. BMC Medical Informatics and Decision Making. 12(1). 28–28. 17 indexed citations
4.
Lenca, Philippe, et al.. (2012). OPTIMONOTONE MEASURES FOR OPTIMAL RULE DISCOVERY. Computational Intelligence. 28(4). 475–504. 6 indexed citations
5.
Lenca, Philippe, Stéphane Lallich, & Benoît Vaillant. (2010). Construction of an off-centered entropy for the supervised learning of imbalanced classes: some first results. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
6.
Lenca, Philippe, Stéphane Lallich, & Benoît Vaillant. (2010). Construction of an Off-Centered Entropy for the Supervised Learning of Imbalanced Classes: Some First Results. Communication in Statistics- Theory and Methods. 39(3). 493–507. 6 indexed citations
7.
Lallich, Stéphane, et al.. (2010). Machine Learning and Knowledge Discovery in Databases. Lecture notes in computer science. 10 indexed citations
8.
Lallich, Stéphane, et al.. (2009). Improving Prediction by Weighting Class Association Rules. 765–770. 2 indexed citations
9.
Lallich, Stéphane, et al.. (2008). Optimization of Self-Organizing Maps Ensemble in Prediction.. 683–688. 1 indexed citations
10.
Do, Thanh‐Nghi, et al.. (2008). Using Local Node Information in Decision Trees: Coupling a Local Labeling Rule with an Off-centered Entropy.. 117–123. 3 indexed citations
11.
Lallich, Stéphane, Benoît Vaillant, & Philippe Lenca. (2007). A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures. Methodology And Computing In Applied Probability. 9(3). 447–463. 8 indexed citations
12.
Lenca, Philippe, Patrick Meyer, Benoît Vaillant, & Stéphane Lallich. (2007). On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European Journal of Operational Research. 184(2). 610–626. 110 indexed citations
13.
Brunet, Guy, Stéphane Lallich, & Alain Bideau. (2006). Analyse généalogique et structure de la population. L’ascendance des natifs de la vallée de la Valserine (Jura français), XVIIe-XXesiècles. Bulletins et Mémoires de la Société d anthropologie de Paris. 18(1-2). 87–102. 1 indexed citations
14.
Lenca, Philippe, Benoît Vaillant, & Stéphane Lallich. (2006). On the robustness of association rules. 7. 1–6. 3 indexed citations
15.
Zighed, Djamel A., Stéphane Lallich, & Fabrice Muhlenbach. (2005). A statistical approach to class separability. Applied Stochastic Models in Business and Industry. 21(2). 187–197. 10 indexed citations
16.
Sebban, Marc, Richard Nock, & Stéphane Lallich. (2003). Stopping criterion for boosting based data reduction techniques: from binary to multiclass problem. Journal of Machine Learning Research. 3. 863–885. 24 indexed citations
17.
Lallich, Stéphane & Olivier Teytaud. (2003). Evaluation et validation de l'interet des regles d'association. 10 indexed citations
18.
Muhlenbach, Fabrice, Stéphane Lallich, & Djamel A. Zighed. (2003). Identifying and Handling Mislabelled Instances. Journal of Intelligent Information Systems. 22(1). 89–109. 88 indexed citations
19.
Rakotomalala, Ricco & Stéphane Lallich. (2002). Construction d'arbres de décision par optimisation. Revue d intelligence artificielle. 16(6). 685–703. 2 indexed citations
20.
Sebban, Marc, Richard Nock, & Stéphane Lallich. (2001). Boosting Neighborhood-Based Classifiers. International Conference on Machine Learning. 505–512. 3 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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