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en:research [2011/11/25 16:59] – sdestercen:research [2015/05/22 09:37] – sdesterc
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-My research mainly concern uncertainty modeling and treatment with the help of imprecise probabilistic approaches (Lower previsions theory, Dempster-Shafer theory, Possibility theory). Roughly speaking, these approaches propose to blend interval and probabilistic methods to deal with situations of severe uncertainty (missing or imprecise data, few available information, expert opinions, non-reliable information, ...). +My research mainly concerns uncertainty modeling and treatment with the help of imprecise probabilistic approaches (Lower previsions theory, Dempster-Shafer theory, Possibility theory). Roughly speaking, these approaches propose to blend interval and probabilistic methods to deal with situations of severe uncertainty (missing or imprecise data, few available information, expert opinions, non-reliable information, ...). 
  
-Inside this theories, most of my research energy is spent in proposing practical solutions to various problems using these methods or in linking the solutions that those theories propose to common problems (information fusion, independence modeling, ...). +Inside these theories, most of my research energy is spent in proposing practical solutions to various problems using these methods or in linking the different solutions each theory proposes to solve common problems (information fusion, independence modeling, ...). 
  
 ====== Practical uncertainty representations ====== ====== Practical uncertainty representations ======
  
-Work with D. Dubois, M. Troffaes, E. Miranda, L. Utkin, E. Chojancki+Work (mainly) benefiting from collaborations and discussions with D. Dubois, M. Troffaes, E. Miranda, L. Utkin, E. Chojnacki, E. Quaeghebeur and I. Sanchez
  
 There exist many practical representations in imprecise probability theories, including possibility distributions, belief functions, imprecise probability assignments, pari-mutuel models, imprecise cumulative distributions (p-boxes), clouds, ... There exist many practical representations in imprecise probability theories, including possibility distributions, belief functions, imprecise probability assignments, pari-mutuel models, imprecise cumulative distributions (p-boxes), clouds, ...
  
-Aside establishing new relations between the properties of several models (i.e. clouds, p-boxes, possibility distributions), we have proposed a model called generalized p-box that models uncertainty by probabilistic bounds over collection of nested sets. Such models appears naturally in elicitation procedures or statistical confidence structures, and first results indicates that generalized p-boxes may be an interesting non-parametric model to handle multivariate problems and/or to handle bipolar information. +Aside establishing new relations between the properties of several models (i.e. clouds, p-boxes, possibility distributions), we have proposed a model called generalized p-box that models uncertainty by probabilistic bounds over collection of nested sets. Such models appear naturally in elicitation procedures or statistical confidence structures, and first results indicates that generalized p-boxes may be an interesting non-parametric model to handle multivariate problems and/or to handle bipolar information. 
  
-Our current focus is to link this model with other models (such as info-gap theory) that use the idea of nested sets to propose solutions to statistical problems.+Our latest research on the topic include the specification of p-boxes limitations, as well as the investigation of some specific cases of comparative probabilities (where only singletons probabilities are qualitatively compared).
  
 ====== Information fusion and combination ====== ====== Information fusion and combination ======
  
-Work with D. Dubois, P. Buche, B. Charnomordic, E. Chojnacki, B. Quost, R. Thomopoulos, F. Sais and T. Burger+Work (mainly) benefiting from collaborations and discussions with D. Dubois, P. Buche, B. Charnomordic, E. Chojnacki, R. Thomopoulos, F. Sais, T. Burger and F. Pichon
  
-Merging information from multiple sources is a common problem in modern systems. Common problems encountered by such merging are to cope with dependent and conflicting sources, and to take account of sources characteristics (reliability, propensity to lie, precision, conflict level, ...).+Merging information from multiple sources is a recurring problem in modern systems. Common problems encountered by such merging are to cope with dependent and conflicting sources and to take account of sources characteristics (reliability, propensity to lie, precision, conflict level, ...).
  
 For practical purposes, we have proposed to use the notion of maximal coherent subsets as a way to deal with conflict among sources, and have applied it to the problem of estimating source reliability form meta-information.  For practical purposes, we have proposed to use the notion of maximal coherent subsets as a way to deal with conflict among sources, and have applied it to the problem of estimating source reliability form meta-information. 
  
-Our current focus on this area is to apply information fusion and combination ideas to the problem of classifier combination. +Our current focus in this area concerns the characterization of inconsistency and reliability degrees resulting either from assumptions about the source of information or from the combination of the different pieces of information. Such degrees can then be used to guide the fusion process. 
  
  
 ====== Uncertainty propagation and (in)dependence modelling ====== ====== Uncertainty propagation and (in)dependence modelling ======
  
-Work with D. Dubois, G. De Cooman E. Chojnacki, J. Baccou and T. Burger+Work (mainly) benefiting from collaborations and discussions with D. Dubois, G. De Cooman, E. Chojnacki, J. Baccou, T. Burger, M. Sallak, M.C.M. Troffaes, F. Coolen, S. Ferson, F. Aguirre and I. Sanchez
  
-A common problem is how to propagate uncertainty analysis in various models. Most of my research in this domain has concerned the propagation of uncertainty model through deterministic model with methods combining Monte-Carlo simulation and interval analysis, with an industrial risk-assessment purpose. +How to propagate uncertainty analysis in various models is an important issue that may face several difficulties. Most of my research in this domain has concerned the propagation of uncertainty model through deterministic functions with methods combining Monte-Carlo simulation and interval analysis, with an industrial risk-assessment purpose. 
  
-Related problems are how to model independence to obtain joint models (and how to compute with such latter models), or how to simulate a given imprecise probabilistic model. +Related problems are how to model independence to obtain tractable joint models (and how to compute with such latter models), or how to simulate a given imprecise probabilistic model. 
  
 +Some of our recent research also deals with the problem of how to efficiently evaluate the reliability when the component reliabilities are uncertain and modelled by imprecise probabilistic knowledge (more speicifically belief functions). 
  
  
-====== Practical applications ====== 
  
-Work with P. Buche, B. Charnomordic, O. Strauss, V. Guillard, E. Chojnacki+====== Learning problems ======
  
-We have applied ideas coming from imprecise probability theories and more generally concerning uncertainty handling to a number of frameworks, including: +Work (mainly) benefiting from collaborations and discussions with B. Quost, T. Denoeux, B. Ben Yaghlane, N. Sutton-Charani, G. Yang, M. Masson, E. Hüllermeier, A. Antonucci, G. Corani, M. Poss and N. Ben Abdallah
-  * Flexible querying in data bases (P. Buche, V. Guillard) +
-  * Signal treatment (O. Strauss) +
-  * Knowledge Engineering (B. Charnomordic, R. Thomopoulos) +
-  * Risk analysis and robust design (E. Chojancki, V. Guillard)+
  
 +Outside of extending some classical classifiers (k-NN methods, Naïve networks) to imprecise probabilistic settings, our work currently focuses on the combination of classifiers, to address both the usual multi-classification problem, as well as more complex problems such as label ranking and multilabel classification.
  
 +One of our current favorite field of investigation is the so-called binary decomposition, where complex problems are decomposed in several binary ones (facilitating the learning but increasing the number of models to learn). In the future, we plan to focus more on active learning topics, where imprecise probabilistic methods can have an important role to play, due to their ability to identify cases where information is missing. 
  
  
 +====== Applications ======
  
 +Work (mainly) benefiting from collaborations and discussions with P. Buche, B. Charnomordic, O. Strauss, V. Guillard, E. Chojnacki, M. Sallak, I. Thouvenin
  
- +We have applied ideas coming from imprecise probability theories and more generally concerning uncertainty handling to a number of frameworks, including: 
 + 
 +  * Flexible querying in data bases (P. Buche, V. Guillard) 
 +  * Signal filtering with kernels (O. Strauss, F. Comby) 
 +  * Knowledge Engineering (B. Charnomordic, R. Thomopoulos) 
 +  * Risk analysis and robust design (E. Chojancki, V. Guillard, M. Sallak) 
 +  * Process modelling (C. Baudrit) 
 +  * Virtual training (I. Thouvenin)

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