VQTree: Vector Quantization for Decision Tree Induction

Authors:
Shlomo Geva;Lawrence Buckingham
Affiliations:
-;-
Venue:
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Year:
2000

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Abstract

We describe a new oblique decision tree induction algorithm. The VQTree algorithm uses Learning Vector Quantization to form a non-parametric model of the training set, and from that obtains a set of hyperplanes which are used as oblique splits in the nodes of a decision tree. We use a set of public data sets to compare VQTree with two existing decision tree induction algorithms, C5.0 and OCl. Our experiments show that VQTree produces compact decision trees with higher accuracy than either C5.0 or OCl on some datasets.