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Dimensionality Reduction in ILP: A Call to Arms

Johannes Fürnkranz

The recent uprise of Knowledge Discovery in Databases (KDD) has underlined the need for machine learning algorithms to be able to tackle large-scale applications that are currently beyond their scope. One way to address this problem is to use technologies for reducing the dimensionality of the learning problem by reducing the hypothesis space and/or reducing the example space. While research in machine learning has devoted considerable attention to such techniques, they have so far been neglected in ILP research. The purpose of this paper is to motivate research in this area and to present some results on windowing techniques.

Citation: Fürnkranz J.: Dimensionality Reduction in ILP: A Call to Arms, Proceedings of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, Nagoya, Japan, August 1997