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

@incollection{citeulike:6008845, 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.}, author = {Atramentov, Anna and Leiva, Hector and Honavar, Vasant}, citeulike-article-id = {6008845}, citeulike-linkout-0 = {http://www.springerlink.com/content/ke19xrf9qhdhuku7}, journal = {Inductive Logic Programming}, keywords = {decision\_tree, relational\_data\_mining}, pages = {38--56}, posted-at = {2009-10-26 10:33:30}, priority = {2}, title = {A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments}, url = {http://www.springerlink.com/content/ke19xrf9qhdhuku7}, year = {2003} }

TY - CHAP ID - citeulike:6008845 L3 - citeulike-article-id:6008845 N2 - 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. JF - Inductive Logic Programming EP - 56 TI - A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments SP - 38 KW - decision_tree KW - relational_data_mining AU - Atramentov, Anna AU - Leiva, Hector AU - Honavar, Vasant PY - 2003/// UR - http://www.springerlink.com/content/ke19xrf9qhdhuku7 ER -