Abstract
XML classification finds many applications, ranging from data integration to e-commerce. However, existing classification algorithms are designed for static XML collections, while modern information systems frequently deal with streaming data that needs to be processed on-line using limited resources. Furthermore, data stream classifiers have to be able to react to concept drifts, i.e., changes of the streams underlying data distribution. In this paper, we propose XStreamClass, an XML classifier capable of processing streams of documents and reacting to concept drifts. The algorithm combines incremental frequent tree mining with partial tree-edit distance and associative classification. XStreamClass was experimentally compared with four state-of-the-art data stream ensembles and provided best average classification accuracy on real and synthetic datasets simulating different drift scenarios.
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Brzezinski, D., Piernik, M. (2014). Adaptive XML Stream Classification Using Partial Tree-Edit Distance. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_2
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DOI: https://doi.org/10.1007/978-3-319-08326-1_2
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