In this paper we investigate the lack of reliability and consistency of those binary rule learners in ILP that employ the one-vs-rest binarisation technique when dealing with multi-class domains. We show that we can learn a simple, consistent and reliable multi-class theory by combining the rules of the multiple one-vs-rest theories into one rule list or set. We experimentally show that our proposed methods produce coherent and accurate rule models from the rules learned by a well known ILP learner Aleph. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Abudawood, T., & Flach, P. A. (2011). Learning multi-class theories in ILP. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6489 LNAI, pp. 6–13). https://doi.org/10.1007/978-3-642-21295-6_4
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