Un état de l'art sur les fonctions de croyance appliquées au traitement de l'information

Un des principaux challenges des Technologies de l'Information et de la Communication concerne l'acces a une information plus fiable, a sa representation informatique ainsi qu'a son traitement. Ainsi, dans le domaine de l'intelligence artificielle, nombreuses sont les recherches qui ont permis d'etendre les formalismes mis en place pour le developpement de systemes intelligents au traitement de l'information imprecise et incertaine. Parmi les modeles etudies, on retrouve les differentes theories des mesures de confiance qui inclut notamment un formalisme capable d'apprehender a la fois imprecision et incertitude, la theorie des fonctions de croyance. Dans cet article, un etat de l'art sur l'application de ce cadre theorique au traitement de l'information est propose. Via la description du modele des croyances transferables introduit par Ph. Smets, les principaux outils associes a la theorie sont presentes. Cette description permet au lecteur neophyte de se familiariser avec les elements mathematiques utiles a la representation et a la manipulation de l'information. Celle-ci peut prendre plusieurs formes (mesures, donnees ou connaissances) selon le type de problemes abordes (reconnaissance des formes, fusion d'informations). Ce modele est suffisamment souple pour etre applique dans de nombreux domaines des Sciences et Technologies de l'Information et de la Communication tels que l'analyse de donnees, le diagnostic, l'aide a la decision, la perception multicapteurs et le traitement d'images.

[1]  Florence Tupin.  Reconnaissance des formes et analyse de scenes en imagerie radar a ouverture synthetique , 1997 .

[2]  George J. Klir,et al.  Uncertainty-Based Information , 1999 .

[3]  Trevor P Martin,et al.  Mass assignment-based induction of decision trees on words , 1998 .

[4]  Takahiko Horiuchi,et al.  Decision Rule for Pattern Classification by Integrating Interval Feature Values , 1998, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Philippe Smets,et al.  The Transferable Belief Model for Quantified Belief Representation , 1998 .

[6]  Jürg Kohlas,et al.  Handbook of Defeasible Reasoning and Uncertainty Management Systems , 2000 .

[7]  Philippe Smets,et al.  Resolving misunderstandings about belief functions , 1992, Int. J. Approx. Reason..

[8]  Thierry Denoeux,et al.  An evidence-theoretic k-NN rule with parameter optimization , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[9]  Jerome J. Braun.  Dempster-Shafer theory and Bayesian reasoning in multisensor data fusion , 2000, SPIE Defense + Commercial Sensing.

[10]  Mongi A. Abidi,et al.  Data fusion in robotics and machine intelligence , 1992 .

[11]  Holger Bracker.  Utilisation de la theorie de dempster/shafer pour la classification d'images satellitaires a l'aide de donnees multisources et multitemporelles , 1996 .

[12]  Ronald R. Yager,et al.  Uncertainty representation using fuzzy measures , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[13]  R. Yager.  Hedging in the Combination of Evidence , 1983 .

[14]  Michael J. Pont,et al.  Application of Dempster-Shafer theory in condition monitoring applications: a case study , 2001, Pattern Recognit. Lett..

[15]  Henk A. Lensen,et al.  Comparison of belief functions and voting method for fusion of mine detection sensors , 2001, SPIE Defense + Commercial Sensing.

[16]  Thierry Denoeux,et al.  Induction of decision trees from partially classified data using belief functions , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[17]  Thierry Denoeux,et al.  Likelihood-based vs. distance-based evidential classifiers , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[18]  Robert Kennes,et al.  Computational aspects of the Mobius transformation of graphs , 1992, IEEE Trans. Syst. Man Cybern..

[19]  Douglas L. Smith.  Detection technologies for mines and minelike targets , 1995, Defense, Security, and Sensing.

[20]  Maria Petrou,et al.  Advanced techniques for fusion of information in remote sensing: an overview , 1999, Remote Sensing.

[21]  R. Yager.  On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[22]  Dennis M. Buede,et al.  A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[23]  D. Dubois,et al.  A NOTE ON MEASURES OF SPECIFICITY FOR FUZZY SETS , 1985 .

[24]  Hong Xu,et al.  Transferable belief model for decision making in the valuation-based systems , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[25]  Thomas M. Strat,et al.  Decision analysis using belief functions , 1990, Int. J. Approx. Reason..

[26]  Ludovic Roux,et al.  Numeric and symbolic data fusion: A soft computing approach to remote sensing images analysis , 1996, Pattern Recognit. Lett..

[27]  Prakash P. Shenoy,et al.  Using Dempster-Shafer's belief-function theory in expert systems , 1992, Defense, Security, and Sensing.

[28]  Hakil Kim,et al.  Evidential reasoning approach to multisource-data classification in remote sensing , 1995, IEEE Trans. Syst. Man Cybern..

[29]  J. Kacprzyk,et al.  USING DEMPSTER-SHAFER ’ S BELIEF-FUNCTION THEORY IN EXPERT SYSTEMS , 1994 .

[30]  Lucas Paletta,et al.  A Comparison of Probabilistic, Possibilistic and Evidence Theoretic Fusion Schemes for Active Object Recognition , 1999, Computing.

[31]  Philippe Smets,et al.  Practical Uses of Belief Functions , 1999, UAI.

[32]  J. Kohlas,et al.  A Mathematical Theory of Hints: An Approach to the Dempster-Shafer Theory of Evidence , 1995 .

[33]  P. L. Bogler,et al.  Shafer-dempster reasoning with applications to multisensor target identification systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[34]  Isabelle Bloch,et al.  Introduction of neighborhood information in evidence theory and application to data fusion of radar and optical images with partial cloud cover , 1998, Pattern Recognit..

[35]  D. Dubois,et al.  Properties of measures of information in evidence and possibility theories , 1987 .

[36]  Philippe Smets,et al.  Computational aspects of the Mobius transformation , 1990, UAI.

[37]  Edward H. Shortliffe,et al.  The Dempster-Shafer theory of evidence , 1990 .

[38]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[39]  P. Vannoorenberghe,et al.  Handling uncertain labels in multiclass problems using belief decision trees , 2002 .

[40]  Alain Appriou.  Formulation et traitement de l'incertain en analyse multi-senseurs , 1993 .

[41]  B. Dubuisson,et al.  Advanced pattern recognition techniques for system monitoring and diagnosis : A survey , 1997 .

[42]  P. Smets.  Decision Making in a Context where Uncertainty is Represented by Belief Functions , 2002 .

[43]  P. R. Gillett,et al.  Monetary unit sampling: a belief-function implementation for audit and accounting applications , 2000, Int. J. Approx. Reason..

[44]  Gregory F. Cooper,et al.  A Bayesian Method for Constructing Bayesian Belief Networks from Databases , 1991, UAI.

[45]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[46]  Thierry Denoeux,et al.  Handling possibilistic labels in pattern classification using evidential reasoning , 2001, Fuzzy Sets Syst..

[47]  Isabelle Bloch,et al.  Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account , 1996, Pattern Recognit. Lett..

[48]  Nikhil R. Pal,et al.  Some classification algorithms integrating Dempster-Shafer theory of evidence with the rank nearest neighbor rules , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[49]  Thierry Denux.  Reasoning with imprecise belief structures , 1999 .

[50]  Simon Parsons,et al.  Addendum to "Current Approaches to Handling Imperfect Information in Data and Knowledge Bases" , 1996, IEEE Trans. Knowl. Data Eng..

[51]  Frank Klawonn,et al.  On the axiomatic justification of Dempster's rule of combination , 1992, Int. J. Intell. Syst..

[52]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[53]  Dromigny-Badin,et al.  5 - Fusion de données radioscopiques et ultrasonores via la théorie de l'évidence , 1997 .

[54]  R. Hartley.  Transmission of information , 1928 .

[55]  R. P. Srivastava,et al.  Audit Decisions Using Belief Functions: A Review , 1997 .

[56]  Eric Lefevre,et al.  Belief function combination and conflict management , 2002, Inf. Fusion.

[57]  Philippe Smets,et al.  Information Content of an Evidence , 1983, Int. J. Man Mach. Stud..

[58]  Antanas Verikas,et al.  Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[59]  Didier Dubois,et al.  Representing partial ignorance , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[60]  Mounia Lalmas,et al.  Representing and retrieving structured documents using the Dempster-Shafer theory of evidence: modelling and evaluation , 1998, J. Documentation.

[61]  Thomas Guyet,et al.  Expert Opinion Extraction from a Biomedical Database , 2017 .

[62]  Lotfi A. Zadeh,et al.  A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination , 1985, AI Mag..

[63]  Bloch.  1 - Incertitude, imprécision et additivité en fusion de données : point de vue historique , 1996 .

[64]  Mathias Bauer,et al.  Approximation algorithms and decision making in the Dempster-Shafer theory of evidence - An empirical study , 1997, Int. J. Approx. Reason..

[65]  M. Rombaut,et al.  Decision Making in Data Fusion Using Dempster-Shafer's Theory , 1997 .

[66]  Philippe Smets,et al.  The Normative Representation of Quantified Beliefs by Belief Functions , 1997, Artif. Intell..

[67]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[68]  Thierry Denoeux,et al.  A neural network classifier based on Dempster-Shafer theory , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[69]  P. Walley,et al.  Upper probabilities based only on the likelihood function , 1999 .

[70]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[71]  D. Gruyer,et al.  Etude du traitement de données imparfaites pour le suivi multi-objets : application aux situations routières , 1999 .

[72]  David A. Bell,et al.  EDM: A General Framework for Data Mining Based on Evidence Theory , 1996, Data Knowl. Eng..

[73]  Johan Schubert.  Creating Prototypes for Fast Classification in Dempster-Shafer Clustering , 1997, ECSQARU-FAPR.

[74]  Jeffrey A. Barnett,et al.  Calculating Dempster-Shafer Plausibility , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[75]  Mats Bengtsson,et al.  Dempster–Shafer clustering using Potts spin mean field theory , 2001, Soft Comput..

[76]  David W. Aha,et al.  Simplifying decision trees: A survey , 1997, The Knowledge Engineering Review.

[77]  Eric Lefevre,et al.  Reply to the Comments of R. Haenni on the paper "Belief functions combination and conflict management , 2003, Inf. Fusion.

[78]  Prakash P. Shenoy,et al.  Propagating Belief Functions with Local Computations , 1986, IEEE Expert.

[79]  Jürg Kohlas,et al.  Model-Based Diagnostics Using Hints , 1995, ECSQARU.

[80]  Philippe Smets,et al.  Data association in multi‐target detection using the transferable belief model , 2001, Int. J. Intell. Syst..

[81]  Padhraic Smyth,et al.  Image database exploration: progress and challenges , 1993 .

[82]  Thierry Denoeux,et al.  Analysis of evidence-theoretic decision rules for pattern classification , 1997, Pattern Recognit..

[83]  P. Walley.  Statistical Reasoning with Imprecise Probabilities , 1990 .

[84]  M.E. El Najjar,et al.  A road reduction method using multi-criteria fusion , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[85]  S. Moral,et al.  COMPLETING A TOTAL UNCERTAINTY MEASURE IN THE DEMPSTER-SHAFER THEORY , 1999 .

[86]  Thierry Denoeux,et al.  Approximating the combination of belief functions using the fast Mo"bius transform in a coarsened frame , 2002, Int. J. Approx. Reason..

[87]  Aldo Franco Dragoni,et al.  Sensor Data Validation for Nuclear Power Plants through Bayesian Conditioning and Dempster's Rule of Combination , 1998, Comput. Artif. Intell..

[88]  Galina L. Rogova,et al.  Combining the results of several neural network classifiers , 1994, Neural Networks.

[89]  Khaled Mellouli,et al.  Belief decision trees: theoretical foundations , 2001, Int. J. Approx. Reason..

[90]  P. Smets.  Application of the transferable belief model to diagnostic problems , 1998 .

[91]  G. G. Wilkinson,et al.  Evidential reasoning in a pixel classification hierarchy—a potential method for integrating image classifiers and expert system rules based on geographic context , 1990 .

[92]  P. Smets.  Data fusion in the transferable belief model , 2000, Proceedings of the Third International Conference on Information Fusion.

[93]  Michel Grabisch,et al.  Using the transferable belief model and a qualitative possibility theory approach on an illustrative example: The assessment of the value of a candidate * , 2001, Int. J. Intell. Syst..

[94]  M. Beynon,et al.  The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling , 2000 .

[95]  Stephane Chauvin.  Evaluation des theories de la decision appliquees a la fusion de capteurs en imagerie satellitaire , 1995 .

[96]  J. Kacprzyk,et al.  Advances in the Dempster-Shafer theory of evidence , 1994 .

[97]  P. Vannoorenberghe,et al.  Strategies for combining conflicting dogmatic beliefs , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[98]  Rolf Haenni,et al.  Modeling Information Retrieval with Probabilistic Argumentation Systems , 1998, BCS-IRSG Annual Colloquium on IR Research.

[99]  Yue Min Zhu,et al.  Study of Dempster-Shafer theory for image segmentation applications , 2002, Image Vis. Comput..

[100]  Geok See Ng,et al.  Data equalisation with evidence combination for pattern recognition , 1998, Pattern Recognit. Lett..

[101]  Thierry Toutin,et al.  Dempster-Shafer theory for multi-satellite remotely sensed observations , 2000, SPIE Defense + Commercial Sensing.

[102]  Jürg Kohlas,et al.  Model-Based Diagnostics and Probabilistic Assumption-Based Reasoning , 1998, Artif. Intell..

[103]  George J. Klir,et al.  Measures of uncertainty in the Dempster-Shafer theory of evidence , 1994 .

[104]  Philippe Smets.  Application of the transferable belief model to diagnostic problems , 1998, Int. J. Intell. Syst..

[105]  Paul Checchin,et al.  Application de la théorie de l'évidence à la combinaison de segmentations en régions , 1999 .

[106]  James C. Bezdek,et al.  Uncertainty measures for evidential reasoning I: A review , 1992, Int. J. Approx. Reason..

[107]  Alain Appriou,et al.  Uncertain Data Aggregation in Classification and Tracking Processes , 1998 .

[108]  E. Mandler,et al.  Combining the Classification Results of Independent Classifiers Based on the Dempster/Shafer Theory of Evidence , 1988 .

[109]  Thierry Denoeux,et al.  Resample and combine: an approach to improving uncertainty representation in evidential pattern classification , 2003, Inf. Fusion.

[110]  Johan Schubert.  Clustering belief functions based on attracting and conflicting metalevel evidence , 2003, ArXiv.

[111]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[112]  Simon Petit Renaud.  Application de la théorie des croyances et des systèmes flous à l'estimation fonctionnelle en présence d'informations incertaines ou imprécises , 1999 .

[113]  T. Denceux,et al.  Combining expert knowledge with data based on belief function theory: an application in waste water treatment , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[114]  Audun Jøsang,et al.  A Logic for Uncertain Probabilities , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[115]  Xavier Briottet,et al.  Presentation and description of two classification methods using data fusion based on sensor management , 2001, Inf. Fusion.

[116]  Eric LEFEVRE,et al.  ABOUT THE USE OF DEMPSTER SHAFER THEORY FOR COLOR IMAGE SEGMENTATION , 2002 .

[117]  Tong Lee,et al.  Probabilistic and Evidential Approaches for Multisource Data Analysis , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[118]  David Harmanec,et al.  Faithful Approximations of Belief Functions , 1999, UAI.

[119]  P. Vannoorenberghe.  Traitement d ’ images et théorie des fonctions de croyance Image processing and belief functions theory , 2003 .

[120]  Thierry Denoeux,et al.  Reasoning with imprecise belief structures , 1999, Int. J. Approx. Reason..

[121]  Philippe Smets,et al.  The Transferable Belief Model , 1994, Artif. Intell..

[122]  Jing-Yu Yang,et al.  Obstacle detection and environment modeling based on multisensor fusion for robot navigation , 1996, Artif. Intell. Eng..

[123]  Philippe Smets,et al.  Fast Algorithms for Dempster-Shafer Theory , 1990, IPMU.

[124]  Wuben Ben Luo,et al.  Using Dempster–Shafer Theory to Represent Climate Change Uncertainties , 1997 .

[125]  J. Kacprzyk,et al.  Aggregation and Fusion of Imperfect Information , 2001 .

[126]  T. Denœux,et al.  Clustering of proximity data using belief functions , 2003 .

[127]  Philippe Smets,et al.  Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem , 1993, Int. J. Approx. Reason..

[128]  Azriel Rosenfeld,et al.  Evidence-based pattern-matching relaxation , 1993, Pattern Recognit..

[129]  Catherine K. Murphy.  Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[130]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[131]  Harry E. Stephanou,et al.  Information fractals for evidential pattern classification , 1990, IEEE Trans. Syst. Man Cybern..

[132]  Prabir Bhattacharya.  On the Dempster-Shafer evidence theory and non-hierarchical aggregation of belief structures , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[133]  James C. Bezdek,et al.  Uncertainty measures for evidential reasoning II: A new measure of total uncertainty , 1993, Int. J. Approx. Reason..

[134]  A. Bastière.  Methods for multisensor classification of airborne targets integrating evidence theory , 1998 .

[135]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[136]  J. Benzecri,et al.  Théorie des capacités , 1956 .

[137]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[138]  Fabrice Janez,et al.  Theory of evidence and non-exhaustive frames of discernment: Plausibilities correction methods , 1998, Int. J. Approx. Reason..