Current prospects on ordinal and monotonic classification

Research areas:

• Methodology - Machine learning

Year:

2016

Type of Publication:

Article

Keywords:

Machine learning, Ordinal classification, Ordinal regression, Monotonic classification, Evaluation measures

Authors:

• Gutiérrez, Pedro Antonio
• García, Salvador

Journal:

Progress in Artificial Intelligence

Volume:

5

Number:

3

Pages:

171-179

Month:

August

ISSN:

2192-6352

BibTex:

@article{ordinalmonotonic2016, author = "Pedro Antonio Guti{\'e}rrez and Salvador Garc{\'i}a", 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.", doi = "10.1007/s13748-016-0088-y", issn = " 2192-6352", journal = "Progress in Artificial Intelligence", keywords = "Machine learning, Ordinal classification, Ordinal regression, Monotonic classification, Evaluation measures", month = "August", number = "3", pages = "171-179", title = "{C}urrent prospects on ordinal and monotonic classification", url = "http://dx.doi.org/10.1007/s13748-016-0088-y", volume = "5", year = "2016", }

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.