S. Lallich

发表

On the robustness of association rules

P. Lenca, B. Vaillant, S. Lallich, 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

Construction of an Off-Centered Entropy for the Supervised Learning of Imbalanced Classes: Some First Results

P. Lenca, B. Vaillant, S. Lallich, 2010 .

Formal Framework for the Study of Algorithmic Properties of Objective Interestingness Measures

Philippe Lenca, Stéphane Lallich, Yannick Le Bras, 2012 .

A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures

Philippe Lenca, Stéphane Lallich, Benoît Vaillant, 2007 .

Évaluation et validation de l'intérêt des règles d'association

O. Teytaud, S. Lallich, 2003 .

Contribution of statistical learning to validation of association rules

Olivier Teytaud, O. Teytaud, S. Lallich, 2001 .

Statistical inference and data mining: false discoveries control

Olivier Teytaud, Stéphane Lallich, O. Teytaud, 2006 .

Du local au global : un nouveau défi pour l'analyse statistique implicative

P. Lenca, S. Lallich, T. Delacroix, 2017 .

Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept

Stéphane Lallich, Ricco Rakotomalala, S. Di Palma, 1999, PKDD.

Quasi-Random resamplings, with applications to rule-samplng, cross-validation and (su-)bagging

S. Gelly, O. Teytaud, S. Lallich, 2006 .

ENHANCING NETWORK INTRUSION CLASSIFICATION THROUGH THE KOLMOGOROV-SMIRNOV SPLITTING CRITERION

Thanh-Nghi Do, P. Lenca, S. Lallich, 2010 .

Identifying and Handling Mislabelled Instances

Fabrice Muhlenbach, Stéphane Lallich, Djamel A. Zighed, 2004, Journal of Intelligent Information Systems.

MaxMin Linear Initialization for Fuzzy C-Means pdf

Jérôme Darmont, Stéphane Lallich, Aybüke Öztürk, 2018, MLDM.

Constrained independence for detecting interesting patterns

Philippe Lenca, Stéphane Lallich, Thomas Delacroix, 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Unsupervised feature construction for improving data representation and semantics

Stéphane Lallich, Julien Velcin, Marian-Andrei Rizoiu, 2013, Journal of Intelligent Information Systems.

A Robustness Measure of Association Rules

Patrick Meyer, Philippe Lenca, Stéphane Lallich, 2010, ECML/PKDD.

Improving Prediction by Weighting Class Association Rules

Stéphane Lallich, Emna Bahri, S. Lallich, 2009, 2009 International Conference on Machine Learning and Applications.

Association Rule Interestingness: Measure and Statistical Validation

Olivier Teytaud, Stéphane Lallich, Elie Prudhomme, 2007, Quality Measures in Data Mining.

Maps Ensemble for Semi-Supervised Learning of Large High Dimensional Datasets

Stéphane Lallich, Elie Prudhomme, S. Lallich, 2008, ISMIS.

On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid

Patrick Meyer, Philippe Lenca, Stéphane Lallich, 2008, Eur. J. Oper. Res..

Outlier Handling in the Neighbourhood-Based Learning of a Continuous Class

Fabrice Muhlenbach, Stéphane Lallich, Djamel A. Zighed, 2004, Discovery Science.

Fast Feature Selection Using Partial Correlation for Multi-vaslued Attributes

Stéphane Lallich, Ricco Rakotomalala, R. Rakotomalala, 2000, PKDD.

Industrial Location Of Innovative Activities And Techno-Industrial Clusters: Astructuralist Approach To National Innovation Systems

Christian Le Bas, Stéphane Lallich, C. L. Bas, 2006 .

Comparison of two topological approaches for dealing with noisy labeling

Fabrice Muhlenbach, Fabien Rico, Djamel A. Zighed, 2015, Neurocomputing.

A statistical approach for separability of classes

Fabrice Muhlenbach, Stéphane Lallich, Djamel A. Zighed, 2005 .

A statistical approach to class separability

Fabrice Muhlenbach, Stéphane Lallich, Djamel A. Zighed, 2005 .

Separability Index in Supervised Learning

Fabrice Muhlenbach, Stéphane Lallich, Djamel A. Zighed, 2002, PKDD.

LOCATION OF INNOVATING ACTIVITIES, INDUSTRIAL STRUCTURE AND TECHNO-INDUSTRIAL CLUSTERS IN THE FRENCH ECONOMY, 1985-1990. EVIDENCE FROM US PATENTING

Stéphane Lallich, Sophie Bergeron, S. Lallich, 1998 .

OPTIMONOTONE MEASURES FOR OPTIMAL RULE DISCOVERY

Philippe Lenca, Stéphane Lallich, Yannick Le Bras, 2012, Comput. Intell..

Association Rule Interestingness Measures: Experimental and Theoretical Studies

Patrick Meyer, Philippe Lenca, Stéphane Lallich, 2007, Quality Measures in Data Mining.

Modeling of the counter-examples and association rules interestingness measures behavior

Philippe Lenca, Stéphane Lallich, Benoît Vaillant, 2006, DMIN.

A Clustering of Interestingness Measures

Philippe Lenca, Stéphane Lallich, Benoît Vaillant, 2004, Discovery Science.

What to expect from a set of itemsets?

P. Lenca, S. Lallich, T. Delacroix, 2022, Inf. Sci..

The use of regional platforms for managing electronic health records for the production of regional public health indicators in France

Thierry Durand, Marie Hélène Metzger, Stéphane Lallich, 2012, BMC Medical Informatics and Decision Making.

A Comparison of Different Off-Centered Entropies to Deal with Class Imbalance for Decision Trees

Thanh-Nghi Do, Nguyen-Khang Pham, Philippe Lenca, 2008, PAKDD.

[Screening psychological distress in breast cancer patients on treatment].

N. Magné, P. Michaud, S. Lallich, 2015, Bulletin du cancer.

Discovering Research Communities by Clustering Bibliographical Data

Fabrice Muhlenbach, Stéphane Lallich, Fabrice Muhlenbach, 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

A New Clustering Algorithm Based on Regions of Influence with Self-Detection of the Best Number of Clusters

Fabrice Muhlenbach, Stéphane Lallich, Fabrice Muhlenbach, 2009, 2009 Ninth IEEE International Conference on Data Mining.

Classifying many-class high-dimensional fingerprint datasets using random forest of oblique decision trees

Thanh-Nghi Do, Philippe Lenca, Stéphane Lallich, 2015, Vietnam Journal of Computer Science.

Classifying Very-High-Dimensional Data with Random Forests of Oblique Decision Trees

Thanh-Nghi Do, Nguyen-Khang Pham, Philippe Lenca, 2009, EGC.

All-Monotony: A Generalization of the All-Confidence Antimonotony

Sorin Moga, Philippe Lenca, Stéphane Lallich, 2009, 2009 International Conference on Machine Learning and Applications.

Mesure formelle de la robustesse des règles d'association

Patrick Meyer, Philippe Lenca, Stéphane Lallich, 2012, Mesurer et évaluer la Qualité des Données et des Connaissances.

Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property

Philippe Lenca, Stéphane Lallich, Yannick Le Bras, 2010, Data Mining.

FCP-Growth: Class Itemsets for Class Association Rules

Stéphane Lallich, Emna Bahri, S. Lallich, 2009, FLAIRS.

Semantic-enriched visual vocabulary construction in a weakly supervised context

Stéphane Lallich, Julien Velcin, Marian-Andrei Rizoiu, 2015, Intell. Data Anal..

Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem

Richard Nock, Marc Sebban, Stéphane Lallich, 2003, J. Mach. Learn. Res..

Using Local Node Information in Decision Trees: Coupling a Local Labeling Rule with an Off-centered Entropy

Thanh-Nghi Do, Nguyen-Khang Pham, Philippe Lenca, 2008, DMIN.

A test to control a region growing process within a hierarchical graph

Jean-Michel Jolion, Fabrice Muhlenbach, Stéphane Lallich, 2003, Pattern Recognit..

A Study of the Robustness of Association Rules

Philippe Lenca, Stéphane Lallich, Jérôme Azé, 2007, DMIN.

A Hybrid Approach of Boosting Against Noisy Data

Stéphane Lallich, Mondher Maddouri, Emna Bahri, 2009, Mining Complex Data.