Abstract
Semi-automatic data mining approaches often yield better results than plain automatic methods, due to the early integration of the user’s goals. For example in the medical domain, experts are likely to favor simpler models instead of more complex models. Then, the accuracy of discovered patterns is often not the only criterion to consider. Instead, the simplicity of the discovered knowledge is of prime importance, since this directly relates to the understandability and the interpretability of the learned knowledge.
In this paper, we present quality measures considering the understandability and the accuracy of (learned) rule bases. We describe a unifying quality measure, which can trade-off small losses concerning accuracy vs. an increased simplicity. Furthermore, we introduce a semi-automatic data mining method for learning understandable and accurate rule bases. The presented work is evaluated using cases from a real world application in the medical domain.
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Atzmueller, M., Baumeister, J., Puppe, F. (2005). Quality Measures and Semi-automatic Mining of Diagnostic Rule Bases. In: Seipel, D., Hanus, M., Geske, U., Bartenstein, O. (eds) Applications of Declarative Programming and Knowledge Management. INAP WLP 2004 2004. Lecture Notes in Computer Science(), vol 3392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11415763_5
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DOI: https://doi.org/10.1007/11415763_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25560-4
Online ISBN: 978-3-540-32124-8
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