 Classification Trees for Problems with Monotonicity ConstraintsRob Potharst and A.J. Feelders ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam Abstract: For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classification problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not produce monotone trees, even if the dataset is completely monotone. This paper surveys the methods that have so far been proposed for generating decision trees that satisfy monotonicity constraints. A distinction is made between methods that work only for monotone datasets and methods that work for monotone and non-monotone datasets alike. Keywords: classification tree; decision tree; monotone; monotonicity constraint; ordinal data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ems:eureri:195 Access Statistics for this paper More papers in ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam Contact information at EDIRC. |
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