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Current prospects on ordinal and monotonic classification

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Abstract

Ordinal classification covers those classification tasks where the different labels show an ordering relation, which is related to the nature of the target variable. In addition, if a set of monotonicity constraints between independent and dependent variables has to be satisfied, then the problem is known as monotonic classification. Both issues are of great practical importance in machine learning. Ordinal classification has been widely studied in specialized literature, but monotonic classification has received relatively low attention. In this paper, we define and relate both tasks in a common framework, providing proper descriptions, characteristics, and a categorization of existing approaches in the state-of-the-art. Moreover, research challenges and open issues are discussed, with focus on frequent experimental behaviours and pitfalls, commonly used evaluation measures and the encouragement in devoting substantial research efforts in specific learning paradigms.

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Acknowledgments

This work is supported by the National Research Projects TIN2014-57251-P and TIN2014-54583-C2-1-R of the Spanish Ministry of Economy and Competitiveness (MINECO), by FEDER Funds and by the P11-TIC-7508 project of the Junta de Andalucía, Spain.

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  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein building, 14071, Córdoba, Spain

    Pedro Antonio Gutiérrez

  2. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Salvador García

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  1. Pedro Antonio Gutiérrez
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  2. Salvador García
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Correspondence to Pedro Antonio Gutiérrez.

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Gutiérrez, P.A., García, S. Current prospects on ordinal and monotonic classification. Prog Artif Intell 5, 171–179 (2016). https://doi.org/10.1007/s13748-016-0088-y

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