Abstract
Inductive Logic Programming (ILP) is a relatively new method in machine learning. Compared with the traditional attribute-value learning methods, it has some advantages (the stronger expressive power and the ease of using background knowledge), but also some weak points. One particular issue is that the theory, techniques and experiences are much less mature for ILP to deal with imperfect data than in the traditional attribute-value learning methods. This paper applies the Rough Set theory to ILP to deal with imperfect data which occur in large real-world applications. We investigate various kinds of imperfect data in ILP and identify a subset of them to tackle. Namely, we concentrate on incomplete background knowledge (where essential predicates/clauses are missing) and on indiscernible data (where some examples belong to both sets of positive and negative training examples), proposing rough problem settings for these cases. The rough settings relax the strict requirements in the standard normal problem setting for ILP, so that rough but useful hypotheses can be induced from imperfect data.
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Liu, C., Zhong, N. (1999). Rough Problem Settings for Inductive Logic Programming. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_21
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DOI: https://doi.org/10.1007/978-3-540-48061-7_21
Publisher Name: Springer, Berlin, Heidelberg
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