An Efficient Triplet-based Algorithm for Evidential Reasoning
  Linear-time computational techniques have been developed for combining
evidence which is available on a number of contending hypotheses. They offer a
means of making the computation-intensive calculations involved more efficient
in certain circumstances. Unfortunately, they restrict the orthogonal sum of
evidential functions to the dichotomous structure applies only to elements and
their complements. In this paper, we present a novel evidence structure in
terms of a triplet and a set of algorithms for evidential reasoning. The merit
of this structure is that it divides a set of evidence into three subsets,
distinguishing trivial evidential elements from important ones focusing some
particular elements. It avoids the deficits of the dichotomous structure in
representing the preference of evidence and estimating the basic probability
assignment of evidence. We have established a formalism for this structure and
the general formulae for combining pieces of evidence in the form of the
triplet, which have been theoretically justified.
