OCAPIS: R package for Ordinal Classification and Preprocessing in Scala

Áreas de investigación:

• Methodology - Ordinal classification

Año:

2019

Tipo de publicación:

Artículo

Palabras clave:

Ordinal classification, Ordinal regression, Data preprocessing, Machine learning, R, Scala

Autores:

• Heredia-Gómez, María Cristina
• García, Salvador
• Gutiérrez, Pedro Antonio
• Herrera, Francisco

Journal:

Progress in Artificial Intelligence

Volumen:

8

Número:

3

Páginas:

287-292

Mes:

September

ISSN:

2192-6360

BibTex:

@article{OCAPIS2019, author = "Mar{\'i}a Cristina Heredia-G{\'o}mez and Salvador Garc{\'i}a and Pedro Antonio Guti{\'e}rrez and Francisco Herrera", abstract = "Ordinal data are those where a natural order exists between the labels. The classification and preprocessing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing in Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two preprocessing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript", doi = "10.1007/s13748-019-00175-1", issn = "2192-6360", journal = "Progress in Artificial Intelligence", keywords = "Ordinal classification, Ordinal regression, Data preprocessing, Machine learning, R, Scala ", month = "September", number = "3", pages = "287-292", title = "{OCAPIS}: {R} package for {O}rdinal {C}lassification and {P}reprocessing in {S}cala", url = "doi.org/10.1007/s13748-019-00175-1", volume = "8", year = "2019", }

Abstract:

Ordinal data are those where a natural order exists between the labels. The classification and preprocessing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing in Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two preprocessing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript