Abstract
Incremental learning of classification rules from data streams with concept drift is considered. We introduce a new algorithm RILL, which induces rules and single instances, uses bottom-up rule generalization based on nearest rules, and performs intensive pruning of the obtained rule set. Its experimental evaluation shows that it achieves better classification accuracy and memory usage than the related rule algorithm VFDR and it is also competitive to decision trees VFDT-NB.
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Deckert, M., Stefanowski, J. (2014). RILL: Algorithm for Learning Rules from Streaming Data with Concept Drift. 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_3
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DOI: https://doi.org/10.1007/978-3-319-08326-1_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08325-4
Online ISBN: 978-3-319-08326-1
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