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The role of feature construction in inductive rule learning

Peter A. Flach, Nada Lavrac, The role of feature construction in inductive rule learning. Proceedings of the ICML2000 workshop on Attribute-Value and Relational Learning: crossing the boundaries. Luc De Raedt, Stefan Kramer, (eds.), pp. 1–11. July 2000. PDF, 64 Kbytes.


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 attribute-value form, which overcomes some of the traditional limitations of propositionalisation approaches.

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