Bernadette Bouchon-Meunier

Bernadette Bouchon-Meunier (IEEE Fellow)

Director of research emeritus at the National Center for Scientific Research

LIP6, University of Pierre & Marie Curie  (France)

IEEE CIS Distinguished Lecturer

Research field: approximate and similarity-based reasoning, as well as the application of fuzzy logic and machine learning techniques to decision-making, data mining, risk forecasting, information retrieval, user modelling, sensorial and emotional information processing.


Bernadette Bouchon-Meunier is a director of research emeritus at the National Centre for Scientific Research, the former head of the department of Databases and Machine Learning in the Computer Science Laboratory of the University Paris 6 (LIP6). She is the Editor-in-Chief of the International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, the (co)-editor of 26 books and the (co)-author of five. She supervised 52 PhD students and 17 of them are now academics, mainly in France, but also in Morocco, Tunisia, Vietnam, Kenya and Colombia.

Co-executive director of the IPMU International Conference held every other year since 1986, she served as the general or program chair of several IEEE International Conferences on Fuzzy Systems and the General Chair of the IEEE Symposium Series on Computational Intelligence (SSCI 2011). She is currently the IEEE Computational Intelligence Society Vice-President for Conferences (2014-2017) and the IEEE France Section Vice-President for Chapters. She is an IEEE Life Fellow and an International Fuzzy Systems Association fellow. She received the IEEE Computational Intelligence Society Meritorious Service Award in 2012. She has been elected an IEEE Computational Intelligence Society Distinguished Lecturer (2014-2016).



Talk 1: Expressiveness and fuzzy modeling

To grasp the complexity of real-world situations is a key issue for all processes of decision-making and control. Fuzzy set theory provides efficient solutions to deal with imprecise, incomplete and uncertain data pertaining to this complexity. In the case of numerical data, in particular, fuzzy set theory enables the user to better understand the results of automatic analysis. It helps him to face the large scale of data available in all kinds of modern environments by participating in the interpretability of results in the management of data, be they videos, temporal data or web elements, for instance. We show various cases where choosing a fuzzy knowledge representation provides interesting tools to summarize, interprete, analyse or score the available data or knowledge, even though we are facing huge and complex information.


Talk 2: Similarity and prototypes in fuzzy data mining

Fuzzy logic provides interesting tools for data mining, mainly because of its ability to represent imperfect information, for instance by means of imprecise categories, measures of resemblance or aggregation methods. This ability is of crucial importance when databases are complex, large, and contain heterogeneous, imprecise, vague, uncertain, incomplete data.

We focus our study on the use of similarities which are key concepts for all attempts to construct human-like automated systems or assistants to human task solving since they are very natural in the human process of categorization underlying many natural capabilities such as language understanding, pattern recognition or decision-making. We point out several types of measures of comparison compatible with cognitive foundations, including measures of similarity and dissimilarity. We show that they can be involved in many steps of the process of data mining, such as clustering, construction of prototypes, fuzzy querying, for instance. We eventually illustrate our discourse by examples of similarities used in real-world data mining problems.