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1998
Springer

Multi-interval Discretization Methods for Decision Tree Learning

9 years 4 months ago
Multi-interval Discretization Methods for Decision Tree Learning
Properly addressing the discretization process of continuos valued features is an important problem during decision tree learning. This paper describes four multi-interval discretization methods for induction of decision trees used in dynamic fashion. We compare two known discretization methods to two new methods proposed in this paper based on a histogram based method and a neural net based method (LVQ). We compare them according to accuracy of the resulting decision tree and to compactness of the tree. For our comparison we used three data bases, IRIS domain, satellite domain and OHS domain (ovariel hyper stimulation).
Petra Perner, Sascha Trautzsch
Added 06 Aug 2010
Updated 06 Aug 2010
Type Conference
Year 1998
Where SSPR
Authors Petra Perner, Sascha Trautzsch
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