RBDT-1: A New Rule-Based Decision Tree Generation Technique

Authors:
Amany Abdelhalim;Issa Traore;Bassam Sayed
Affiliations:
Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 3055 STN CSC, Victoria, Canada V8W 3P6;Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 3055 STN CSC, Victoria, Canada V8W 3P6;Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 3055 STN CSC, Victoria, Canada V8W 3P6
Venue:
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
Year:
2009

Citing 8
Cited 1

Using concept learning for knowledge acquisition

International Journal of Man-Machine Studies
Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Learning decision trees from decision rules: a method and initial results from a comparative study

Journal of Intelligent Information Systems - Special issue on methodologies for intelligent systems
On learning decision structures

Fundamenta Informaticae
An Empirical Comparison of Selection Measures for Decision-Tree Induction

Machine Learning
Learning Problem-Oriented Decision Structures from Decision Rule: The AQDT-2 System

ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Blind paraunitary equalization

Signal Processing

Inducing decision trees with an ant colony optimization algorithm

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most of the methods that generate decision trees use examples of data instances in the decision tree generation process. This paper proposes a method called "RBDT-1 "- rule based decision tree -for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. The method'sgoal is to create on-demand a short and accurate decision tree from a stable or dynamically changing set of rules. We conduct a comparative study of RBDT-1 with three existing decision tree methods based on different problems. The outcome of the study shows that RBDT-1 performs better than AQDT-1 andAQDT-2 which are rule-based decision tree methods in terms of tree complexity (number of nodes and leaves in the decision tree). It is also shown that RBDT-1 performs equally well in terms of tree complexity compared with C4.5 , which generates a decision tree from data examples.