LINUS is an ILP learner which incorporates existing attribute-value learning systems. The idea is to transform a restricted class of ILP problems into propositional form and solve the transformed learning problem with an attribute-value learning algorithm. The propositional learning result is then re-transformed into the first-order language. On the one hand, this approach enhances the propositional learners with the use of background knowledge and the more expressive hypothesis language. On the other hand, it enables the application of successful propositional learners in a first-order framework. As various propositional learners can be integrated and accessed via LINUS, LINUS also qualifies as an ILP toolkit offering several learning algorithms with their specific strengths. The present distribution of LINUS provides interfaces to the attribute-value learners ASSISTANT, NEWGEM, and CN2. Other propositional learners may be added. L! INUS can be run in two modes. Running in CLASS mode, it corresponds to an enhanced attribute-value learner. In RELATION mode, LINUS behaves as an ILP system.

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  1. Lavrač, Nada; Džeroski, Sašo: Weakening the language bias in LINUS (1994)