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.
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Abdelhalim, A., Traore, I., Sayed, B. (2009). RBDT-1: A New Rule-Based Decision Tree Generation Technique. In: Governatori, G., Hall, J., Paschke, A. (eds) Rule Interchange and Applications. RuleML 2009. Lecture Notes in Computer Science, vol 5858. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04985-9_12
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DOI: https://doi.org/10.1007/978-3-642-04985-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04984-2
Online ISBN: 978-3-642-04985-9
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