A Nearest Hyperrectangle Monotonic Learning Method

  • Javier García
  • José-Ramón Cano
  • Salvador García
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

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.

Keywords

Monotonic classification Instance-based learning Rule induction Nested generalized examples 

Notes

Acknowledgments

This work was partially supported by the Spanish Ministry of Science and Technology under project TIN2014-57251-P and the Andalusian Research Plans P11-TIC-7765, P10-TIC-6858.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Javier García
    • 1
  • José-Ramón Cano
    • 2
  • Salvador García
    • 3
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Computer ScienceUniversity of Jaén, EPS of LinaresLinaresSpain
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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