Abstract. Current inductive machine learning algorithms typically use greedy search with limited lookahead.
This prevents them to detect significant conditional dependencies between the attributes that describe training
objects. Instead of myopic impurity functions and lookahead, we propose 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 decision trees, using RELIEFF as an estimator
of attributes at each selection step.