YaDT: Yet another Decision Tree builder

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2004, Pages 260-265

  Ruggieri, Salvatore ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SOFTWARE; DATABASE SYSTEMS; HEURISTIC METHODS; METADATA; SET THEORY; TREES (MATHEMATICS);

DECISION TREE ALGORITHMS; DECISION TREE BUILDERS; PRUNING STEPS; YADT;

DECISION THEORY;

EID: 16244390201     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. C. Borgelt. A decision tree plug-in for DataEngine. In Proc. 6th European Congress on Intelligent Tech niques and Soft Computing, volume 2, pages 1299-1303, 1998. Verlag Mainz, dti version 3.12 from http://-fuzzy.cs.uni-magdeburg.de/~borgelt.
2. E. K. C. Blake and C. Merz. UCI repository of machine learning databases http://www.ics.uci.edu/-~mlearn/mlrepository.html, 2003.
3. U. M. Fayyad and K. B. Irani. On the handling of continuous-valued attributes in decision tree generation. Machine Learning, 8:87-102, 1992.
4. J. E. Gehrke, R. Ramakrishnan, and V. Ganti. RainForest - A framework for fast decision tree construction of large datasets. Data Mining and Knowledge Discovery, 4(2/4): 127-162, 2000.
5. T. Lim, W. Loh, and Y. Shih. A comparison of prediction accuracy, complexity, and training time of thirthy-tree old and new classification algorithms. Machine Learning Journal, 40:203-228, 2000.
6. Predictive Model Markup Language (PMML). Version 2.0. http://www.dmg.org.
7. Prudsys AG. The XELOPES library (eXtEnded Library fOr Prudsys Embedded Solutions) v. 1.1 for C++, May 2003. http://www.prudsys.com.
8. KDD Cup Competion Data Sets. Online documentation, 1999. http://-www.epsilon.com/new/1datamining.html.
9. Quest synthetic data generation code. On-line documentation, Visited in May 2003. http://-www.almaden.ibm.com/software/quest.
10. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
11. S. Ruggieri. Efficient C4.5. IEEE Transactions on Knowledge and Data Engineering, 14:438-444, 2002.
12. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan & Kaufmann, 2000. Weka version 3.2.3 from http://www.cs.waikato.ac.nz/m1/weka.