  Case Based Reasoning and particularly Estimation by Analogy, has been used in
a number of problem-solving areas, such as cost estimation. Conventional
methods, despite the lack of a sound criterion for choosing nearest projects,
were based on estimation using a fixed and predetermined number of neighbors
from the entire set of historical instances. This approach puts boundaries to
the estimation ability of such algorithms, for they do not take into
consideration that every project under estimation is unique and requires
different handling. The notion of distributions of distances together with a
distance metric for distributions help us to adapt the proposed method (we call
it DD-EbA) each time to a specific case that is to be estimated without loosing
in prediction power or computational cost. The results of this paper show that
the proposed technique achieves the above idea in a very efficient way.
