Laboratoire de Génie Informatique et d’Automatique de l’Artois

Ph.D. thesis of Ahmed SAMET

Belief function theory : application of data mining tools for imperfect data treatment

Starting date: 1 October 2011
Keywords: Data mining, Belief function theory, conflict management, Associative classification
Advising:

This thesis explores the relation between two domains which are the Belief Function Theory (BFT) and data mining. Two main interactions between those domain have been pointed out.The first interaction studies the contribution of the generic associative rules in the BFT. We were interested in managing conflict in case of fusing conflictual information sources. A new approach for conflict management based on generic association rules has been proposed called ACM.The second interation studies imperfect databases such as evidential databases. Those kind of databases, where information is represented by belief functions, are studied in order to extract hidden knowledges using data mining tools. The extraction of those knowledges was possible thanks to a new definition to the support and the confidence measures. Those measures were integrated into a new evidential associative classifier called EDMA.

Involved research axes:

Partners

Defense

Defense took place the 03/12/2014 am31 10:00 Prestige room - FSA - Béthune

Jury:

  • Président Didier DUBOIS Université Paul Sabatier
  • Rapporteur Arnaud MARTIN Université de Rennes 1
  • Rapporteur Zied ELOUEDI Institut Supérieur de Gestion de Tunis
  • Examinateur Mohamed Mohsen GAMMOUDI ISAM de la Manouba
  • Advisor Eric LEFEVRE Université d'Artois
  • Advisor Sadok BEN YAHIA Faculté des Sciences de Tunis