The purpose of this paper is to show that a well known machine
learning technique based on Decision Trees can be effectively used
to select the best approach (in terms of efficiency) in an
algorithm portfolio for a particular case study: the Bid
Evaluation Problem (BEP) in Combinatorial Auctions. In particular,
we are interested in deciding when to use a Constraint Programming
(CP) approach and when an Integer Programming (IP) approach, on
the basis of the structure of the instance considered. Different
instances of the same problem present a different
structure, and one aspect (e.g. feasibility or optimality) can
prevail on the other. We have extracted from a set of BEP
instances, a number of parameters representing the instance
structure. Some of them (few indeed) precisely identify the best
strategy and its corresponding tuning to be used to face that
instance. We will show that this approach is very promising, since
it identifies the most efficient algorithm in the 90% of the cases.
