An Inductive Learning System for XML Documents.

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.