Abstract
Rule learning is among the oldest machine learning techniques and it has been used in numerous applications. When optimal prediction quality of induced models is the primary goal then rule learning is not necessarily the best choice. For such applications one may often expect better results from complex models constructed by more modern machine learning approaches likesupport vector machines or random forests. The key quality of rule learning has been—and still is—its inherent simplicity and the human understandability of the induced models and patterns. Research results clearly demonstrate that rule learning, together with decision tree learning, is an essential part of the knowledge discovery process.
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Fürnkranz, J., Gamberger, D., Lavrač, N. (2012). Selected Applications. In: Foundations of Rule Learning. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75197-7_12
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DOI: https://doi.org/10.1007/978-3-540-75197-7_12
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