A Nearest Hyperrectangle Monotonic Learning Method

Abstract

We can find real prediction learning problems whose class attribute is represented by ordinal values that should increase with some of the explaining attributes. They are known as classification problems with monotonicity constraints. In this contribution, our goal is to formalize the nearest hyperrectangle learning approach to manage monotonicity constraints. The idea behind it is to retain objects in $\mathbb {R}^n$, which can be either single points or hyperrectangles or rules into a combined model. The approach is checked with experimental analysis involving wide range of monotonic data sets. The results reported, verified by nonparametric statistical tests, show that our approach is very competitive with well-known techniques for monotonic classification.