A multi-relational decision tree learning algorithm - Implementation and experiments

Anna Atramentov, Hector Leiva, Vasant Honavar

Research output: Contribution to journalConference articlepeer-review

28 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)38-56
Number of pages19
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
StatePublished - 2003
Event13th International Conference, ILP 2003 - Szeged, Hungary
Duration: Sep 29 2003Oct 1 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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