We discuss an approach to constructing composite features during the
induction of decision trees.  The composite features correspond to
m-of-n concepts.  There are three goals of this research.  First, we 
explore a family of greedy methods for building m-of-n concepts (one 
of which, GS, is described in this paper).  Second, we show how these 
concepts can be formed as internal nodes of decision trees, serving 
as a bias to the learner. Finally, we evaluate the method on several 
artificially generated and naturally occurring data sets to determine 
the effects of this bias.