  Possibility and probability theories are alternative and complementary ways
to deal with uncertainty, which has motivated over the last years an interest
for the study of ways to transform probability distributions into possibility
distributions and conversely. This paper studies the advantages and
shortcomings of two well-known discrete probability to possibility
transformations: the optimal transformation and the symmetrical transformation,
and presents a novel parametric family of probability to possibility
transformations which generalizes them and alleviate their shortcomings,
showing a big potential for practical application. The paper also introduces a
novel fuzzy measure of specificity for probability distributions based on the
concept of fuzzy subsethood and presents a empirical validation of the
generalized transformation usefulness applying it to the text authorship
attribution problem.
