This paper describes an approach for deriving classification knowledge
from databases, taking into account user preferences. These preferenc
es especially concern the trade-off between different kinds of costs a
nd performance indicators of the classification scheme to be developed
. We analyze what knowledge, provided by the user, can be used at vari
ous stages of the machine learning process to influence the developmen
t of the classifier. We restrict ourselves in this paper mainly to the
generation of classification trees.