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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 3/2016

01.08.2016 | Regular Paper

Current prospects on ordinal and monotonic classification

verfasst von: Pedro Antonio Gutiérrez, Salvador García

Erschienen in: Progress in Artificial Intelligence | Ausgabe 3/2016

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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.
Vorheriger Artikel Pattern mining: current status and emerging topics
Nächster Artikel Development of a clinical decision support system for antibiotic management in a hospital environment

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Metadaten
Titel
Current prospects on ordinal and monotonic classification
verfasst von
Pedro Antonio Gutiérrez
Salvador García
Publikationsdatum
01.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 3/2016
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-016-0088-y

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