Current inductive machine learning algorithms typically use greedy sea
rch with limited lookahead. This prevents them to detect significant c
onditional dependencies between the attributes that describe training
objects. Instead of myopic impurity functions and lookahead, we propos
e to use RELIEFF an extension of RELIEF developed by Kira and Rendell
[10, 11], for heuristic guidance of inductive learning algorithms. We
have reimplemented Assistant, a system for top down induction of decis
ion trees, using RELIEFF as an estimator of attributes at each selecti
on step. The algorithm is tested on several artificial and several rea
l world problems and the results are compared with some other well kno
wn machine learning algorithms. Excellent results on artificial data s
ets and two real world problems show the advantage of the presented ap
proach to inductive learning.