Abstract
In many classiffication problems the domains of the attributes and the classes are linearly orderded. For such problems the classiffication rule often needs to be order-preserving or monotone as we call it. Since the known decision tree methods generate non-monotone trees, these methods are not suitable for monotone classiffication problems. We provide an order-preserving tree-generation algorithm for multi-attribute classiffication problems with k linearly ordered classes, and an algorithm for repairing non-monotone decision trees. The performance of these algorithms is tested on random monotone datasets.
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Potharst, R., Bioch, J.C. (1999). A Decision Tree Algorithm for Ordinal Classification. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_16
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DOI: https://doi.org/10.1007/3-540-48412-4_16
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