Abstract: We describe a monotone classification algorithm called MOCA that attempts to minimize the mean absolute prediction error for classification ...

We describe a monotone classification algorithm called MOCA that attemptsto minimize the mean absolute prediction error for classification problems with ...

This paper surveys the methods that have so far been proposed for generating decision trees that satisfy monotonicity constraints. A distinction is made between ...

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A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering · Bayesian Non-Parametric Ordinal Regression Under a Monotonicity Constraint.

Such relations are called monotone. We discuss two nonparametric approaches to monotone classification: osdl and moca. Our conjecture is that both methods ...

Abstract—We consider the problem of ordinal classification with mono- tonicity constraints. It differs from usual classification by handling back-.

Nov 17, 2018 · • MOCA ([28]). MOCA is a nonparametric monotone classification algo- rithm that attempts to minimize the mean absolute prediction error for.

In classification with monotonicity con- straints, it is assumed that the class label should increase with increasing values on the attributes.