A Framework for Learning Comprehensible Theories in XML Document Classification

XML has become the universal data format for a wide variety of information systems. The large number of XML documents existing on the web and in other information storage systems makes classification an important task. As a typical type of semistructured data, XML documents have both structures and contents. Traditional text learning techniques are not very suitable for XML document classification as structures are not considered. This paper presents a novel complete framework for XML document classification. We first present a knowledge representation method for XML documents which is based on a typed higher order logic formalism. With this representation method, an XML document is represented as a higher order logic term where both its contents and structures are captured. We then present a decision-tree learning algorithm driven by precision/recall breakeven point (PRDT) for the XML classification problem which can produce comprehensible theories. Finally, a semi-supervised learning algorithm is given which is based on the PRDT algorithm and the cotraining framework. Experimental results demonstrate that our framework is able to achieve good performance in both supervised and semi-supervised learning with the bonus of producing comprehensible learning theories