Abstract
We describe an efficient implementation (MRDTL-2) of the Multi-relational decision tree learning (MRDTL) algorithm [23] which in turn was based on a proposal by Knobbe et al. [19]. We describe some simple techniques for speeding up the calculation of sufficient statistics for decision trees and related hypothesis classes from multi-relational data. Because missing values are fairly common in many real-world applications of data mining, our implementation also includes some simple techniques for dealing with missing values. We describe results of experiments with several real-world data sets from the KDD Cup 2001 data mining competition and PKDD 2001 discovery challenge. Results of our experiments indicate that MRDTL is competitive with the state-of-the-art algorithms for learning classifiers from relational databases.
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Atramentov, A., Leiva, H., Honavar, V. (2003). A Multi-relational Decision Tree Learning Algorithm – Implementation and Experiments. In: Horváth, T., Yamamoto, A. (eds) Inductive Logic Programming. ILP 2003. Lecture Notes in Computer Science(), vol 2835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39917-9_5
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