An In-Depth Look at Information Fusion Rules & the Unification of Fusion Theories

Details An In-Depth Look at Information Fusion Rules & the Unification of Fusion Theories An In-Depth Look at Information Fusion Rules & the Unification of Fusion Theories

2004-10-14

arxiv.org/abs/cs/0410033v2

Partially published in Review of the Air Force Academy (The Scientific Informative Review), Brasov, No. 2, pp. 31-40, 2006.

Computer Science

Artificial Intelligence

27 pages. To be presented at NASA Langley Research Center (Hampton, Virginia), on November 5th, 2004

Scientific paper

This paper may look like a glossary of the fusion rules and we also introduce new ones presenting their formulas and examples: Conjunctive, Disjunctive, Exclusive Disjunctive, Mixed Conjunctive-Disjunctive rules, Conditional rule, Dempster's, Yager's, Smets' TBM rule, Dubois-Prade's, Dezert-Smarandache classical and hybrid rules, Murphy's average rule, Inagaki-Lefevre-Colot-Vannoorenberghe Unified Combination rules [and, as particular cases: Iganaki's parameterized rule, Weighting Average Operator, minC (M. Daniel), and newly Proportional Conflict Redistribution rules (Smarandache-Dezert) among which PCR5 is the most exact way of redistribution of the conflicting mass to non-empty sets following the path of the conjunctive rule], Zhang's Center Combination rule, Convolutive x-Averaging, Consensus Operator (Josang), Cautious Rule (Smets), ?-junctions rules (Smets), etc. and three new T-norm & T-conorm rules adjusted from fuzzy and neutrosophic sets to information fusion (Tchamova-Smarandache). Introducing the degree of union and degree of inclusion with respect to the cardinal of sets not with the fuzzy set point of view, besides that of intersection, many fusion rules can be improved. There are corner cases where each rule might have difficulties working or may not get an expected result.

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