Abstract
We propose a new algorithm for learning isotonic classification trees. It relabels non-monotone leaf nodes by performing the isotonic regression on the collection of leaf nodes. In case two leaf nodes with a common parent have the same class after relabeling, the tree is pruned in the parent node. Since we consider problems with ordered class labels, all results are evaluated on the basis of L 1 prediction error. We experimentally compare the performance of the new algorithm with standard classification trees.
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van de Kamp, R., Feelders, A., Barile, N. (2009). Isotonic Classification Trees. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, JF. (eds) Advances in Intelligent Data Analysis VIII. IDA 2009. Lecture Notes in Computer Science, vol 5772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03915-7_35
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DOI: https://doi.org/10.1007/978-3-642-03915-7_35
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