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ILP
2003
Springer

A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments

13 years 8 months ago
A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments
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-theart algorithms for learning classifiers from relational databases.
Anna Atramentov, Hector Leiva, Vasant Honavar
Added 07 Jul 2010
Updated 07 Jul 2010
Type Conference
Year 2003
Where ILP
Authors Anna Atramentov, Hector Leiva, Vasant Honavar
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