An inductive learning system for XML documents. (English) Zbl 1136.68509
Blockeel, Hendrik (ed.) et al., Inductive logic programming. 17th international conference, ILP 2007, Corvallis, OR, USA, June 19–21, 2007. Revised selected papers. Berlin: Springer (ISBN 978-3-540-78468-5/pbk). Lecture Notes in Computer Science 4894. Lecture Notes in Artificial Intelligence, 292-306 (2008).
Summary: 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.
For the entire collection see [Zbl 1132.68005].
For the entire collection see [Zbl 1132.68005].
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68T27 | Logic in artificial intelligence |
| 68T30 | Knowledge representation |
Keywords:
higher-order logic; knowledge representation; XML documents; precision-recall; decision-tree learningReferences:
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