Abstract
This paper presents a complete inductive learning system that aims to produce comprehensible theories for XML document classifications. The knowledge representation method is based on a higher-order logic formalism which is particularly suitable for structured-data learning systems. A systematic way of generating predicates is also given. The learning algorithm of the system is a modified standard decision-tree learning algorithm driven by predicate/recall breakeven point. Experimental results on XML version of Reuters dataset show that this system is able to produce comprehensible theories with high precision/recall breakeven point values.
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Wu, X. (2008). An Inductive Learning System for XML Documents. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds) Inductive Logic Programming. ILP 2007. Lecture Notes in Computer Science(), vol 4894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78469-2_28
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DOI: https://doi.org/10.1007/978-3-540-78469-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78468-5
Online ISBN: 978-3-540-78469-2
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