| Propagating belief functions in qualitative Markov trees G Shafer, PP Shenoy, K Mellouli International Journal of Approximate Reasoning 1 (4), 349-400, 1987 | 291 | 1987 |
| Assessing sensor reliability for multisensor data fusion within the transferable belief model Z Elouedi, K Mellouli, P Smets IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34 …, 2004 | 250 | 2004 |
| Belief decision trees: theoretical foundations Z Elouedi, K Mellouli, P Smets International Journal of Approximate Reasoning 28 (2-3), 91-124, 2001 | 173 | 2001 |
| An NSGA-II algorithm for the green vehicle routing problem J Jemai, M Zekri, K Mellouli Evolutionary Computation in Combinatorial Optimization: 12th European …, 2012 | 128 | 2012 |
| Belief function independence: I. The marginal case BB Yaghlane, P Smets, K Mellouli International Journal of Approximate Reasoning 29 (1), 47-70, 2002 | 113* | 2002 |
| A new similarity measure based on edge counting T Slimani, BB Yaghlane, K Mellouli International Journal of Computer and Information Engineering 2 (11), 3851-3855, 2008 | 82 | 2008 |
| Inference in directed evidential networks based on the transferable belief model BB Yaghlane, K Mellouli International Journal of Approximate Reasoning 48 (2), 399-418, 2008 | 80 | 2008 |
| On the propagation of beliefs in networks using the Dempster-Shafer theory of evidence K Mellouli University of Kansas, 1987 | 76 | 1987 |
| Anytime propagation algorithm for min-based possibilistic graphs N Ben Amor, S Benferhat, K Mellouli Soft Computing 8, 150-161, 2003 | 64 | 2003 |
| Information affinity: A new similarity measure for possibilistic uncertain information I Jenhani, N Ben Amor, Z Elouedi, S Benferhat, K Mellouli Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 9th …, 2007 | 62 | 2007 |
| Naïve possibilistic network classifiers B Haouari, NB Amor, Z Elouedi, K Mellouli Fuzzy Sets and Systems 160 (22), 3224-3238, 2009 | 60 | 2009 |
| Directed evidential networks with conditional belief functions BB Yaghlane, P Smets, K Mellouli Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 7th …, 2003 | 59 | 2003 |
| A theoretical framework for possibilistic independence in a weakly ordered setting NB Amor, K Mellouli, S Benferhat, D Dubois, H Prade International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2002 | 50 | 2002 |
| Naive possibilistic classifiers for imprecise or uncertain numerical data M Bounhas, MG Hamed, H Prade, M Serrurier, K Mellouli Fuzzy Sets and Systems 239, 137-156, 2014 | 48 | 2014 |
| Clustering approach using belief function theory S Ben Hariz, Z Elouedi, K Mellouli Artificial Intelligence: Methodology, Systems, and Applications: 12th …, 2006 | 47 | 2006 |
| Une extension de mesure de similarité entre les concepts d’une ontologie T Slimani, B BenYaghlane, K Mellouli International conference on sciences of electronic, technologies of …, 2007 | 46 | 2007 |
| Possibilistic classifiers for numerical data M Bounhas, K Mellouli, H Prade, M Serrurier Soft Computing 17, 733-751, 2013 | 45 | 2013 |
| A new algorithm for mining frequent itemsets from evidential databases MAB Tobji, BB Yaghlane, K Mellouli Proceedings of IPMU 8, 1535-1542, 2008 | 45 | 2008 |
| Qualitative possibilistic influence diagrams based on qualitative possibilistic utilities W Guezguez, NB Amor, K Mellouli European Journal of Operational Research 195 (1), 223-238, 2009 | 40 | 2009 |
| Elicitation of expert opinions for constructing belief functions AB Yaghlane, T Denœux, K Mellouli Uncertainty and intelligent information systems, 75-89, 2008 | 39 | 2008 |
