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 hy-perplanes 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 OC1. Our experiments show that VQTree produces compact decision trees with higher accuracy than either C5.0 or OC1 on some datasets.
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Geva, S., Buckingham, L. (2000). VQTree: Vector Quantization for Decision Tree Induction. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_41
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DOI: https://doi.org/10.1007/3-540-45571-X_41
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