The role of feature construction in inductive rule learning
Peter A. Flach, Raedt Luc De, Nada Lavrač, Stefan Kramer · 2000
. This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in the following subtasks: rule construction, body construction, and feature construction. Each of these subtasks may have its own declarative bias, search strategies, and heuristics. In particular, we argue that feature construction is a crucial notion in explaining the relations between attribute-value rule learning and inductive logic programming (ILP). We demonstrate this by a general method for transforming ILP problems to attributevalue form, which overcomes some of the traditional limitations of propositionalisation approaches. 1 Introduction Rule learning tasks are typically approached as search problems, and hence the construction of candidate hypotheses is a crucial task in inductive rule learning algorithms. Traditionally, hypotheses are constructed by constructing rules...