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In many applications of data mining we know beforehand that the response variable should be increasing (or decreasing) in the attributes. Such relations between response and attributes are called monotone. In this paper we present a new algorithm to compute an optimal monotone classification of a data set for convex loss functions. Moreover, we show how the algorithm can be extended to compute all optimal monotone classifications with little additional effort. Monotone relabeling is useful fordoi:10.1109/icdm.2010.92 dblp:conf/icdm/Feelders10 fatcat:wo2yhsjeyrar7c6rlqphiibcga